https://www.causeweb.org/wiki/chance/api.php?action=feedcontributions&user=Simon66217&feedformat=atomChanceWiki - User contributions [en]2024-03-28T10:27:58ZUser contributionsMediaWiki 1.40.0-alphahttps://www.causeweb.org/wiki/chance/index.php?title=Chance_News_112&diff=21982Chance News 1122017-09-11T18:36:52Z<p>Simon66217: /* Forsooth */</p>
<hr />
<div>'''''Under construction'':''' August 21, 2017 to ...<br />
==Quotations==<br />
<br />
==Forsooth==<br />
"Of the 36 applicants that were interviewed, 20 were ultimately promoted... . Among the promoted individuals, 62 percent were female and 38 percent were male."<br />
<br />
<div align=right> in: [http://www.jdsupra.com/legalnews/woefully-thin-statistics-doom-adverse-41363/ “Woefully thin statistics” doom adverse impact claim], JDSUPRA.com, 24 August 2017</div><br />
<br />
----<br />
The graphic below appears in: [https://www.inverse.com/article/36156-divorce-rate-study-americans-bartenders-flight-attendants New study reveals bartenders, casino workers most likely to get divorced], ''Inverse Culture'', 5 September 2017<br />
[[File:Divorce scatter.png|600px|frameless|center]]<br />
<br />
<br />
(Note: The scatterplot was [http://flowingdata.com/2017/07/25/divorce-and-occupation originally created by ''FlowingData''], where the relationship is correctly<br />
described as "downward slopey"; the caption above is from the ''Inverse Culture'' article.)<br />
----<br />
Facebook advertises that it "can reach 41 million 18 to 24-year-olds in the United States and 60 million 25- to 34-year-olds." But according to the U.S. Census, there are only 31 million and 45 million total people in those two demographic groups. Details are in [https://www.nytimes.com/2017/09/06/business/media/facebook-advertisers.html this new York Times article.]<br />
<br />
==Lecture on football probability==<br />
Margaret Cibes sent a link to the following YouTube video:<br />
<br />
:[https://www.youtube.com/watch?v=G5FNHE_EcRA John Urschel-NFL Math Whiz: Real Sports Full Segment (HBO)]<br />
<br />
It features John Urschel, an offensive for the NFL's Baltimore Ravens, who is also studying applied mathematics at MIT. The video begins with John at a chalkboard using a decision tree to analyze a one-point vs. two-point conversion late in a football game.<br />
<br />
John is already a published mathematician, as described in [https://math.mit.edu/~urschel/notices.pdf this 2016 article] from the ''Notices of the AMS''.<br />
<br />
==Redefining statistical significance==<br />
[https://news.uchicago.edu/article/2017/09/01/scholars-take-aim-false-positives-research Scholars take aim at false positives in research]<br><br />
by Thomas Gaulkin, ''UChicagoNews'', 1 September 2017<br />
<br />
University Chicago economist John List is one of 72 collaborators whose commentary, <br />
[https://www.nature.com/articles/s41562-017-0189-z.epdf?author_access_token=Eb6x88zTNQ7PuVxPt1CpXdRgN0jAjWel9jnR3ZoTv0PlqY8PQKtlL9OP0czNSVZ5rodrqWv-lxLd4whdDH-qvHpF5PQtT1U4AblMVaKnbDH0ctY2yThyrB_ccetKNmK4sasDTgzcxT5_u2wTJ8C6sg%3D%3D Redfine statistical significance], was just published in ''Nature Human Behavior''. The subtitle reads, <br />
"We propose to change the default P-value threshold for statistical significance from 0.05 to 0.005 for claims of <br />
new discoveries."<br />
<br />
To be continued...<br />
<br />
==How bad/good were the predictions about Hurricane Irma==<br />
[https://www.nytimes.com/2017/09/10/us/forecast-irma-shift-florida.html Irma Shifting Forecast: It's All a Matter of Probability]. John Schwartz, The New York Times, September 10, 2017.<br />
<br />
How surprising is it that Irma is heading up the "wrong" coast of Florida? Well, it changed the plans of one expert.<br />
<br />
<blockquote>Brian McNoldy, a researcher at the University of Miama and respected blogger on tropical storms and hurricanes, decided on Thursday to evacuate from South Florida with friends and his two dogs and drive to the Tampa area.</blockquote><br />
<br />
Dr. McNoldy ended up travelling back to Miami once the forecast changed, showing Irma smashing into the west coast of Florida rather than hitting Miami dead on.<br />
<br />
Was this a failure of the statistical model? Florida is such a skinny state that Dr. McNoldy admits that predicting where any hurricane will hit is problematic.<br />
<br />
<blockquote>"A hundred miles is the difference between the east coast and the west coast-but a hundred miles in a three-day forecast is really good."</blockquote><br />
<br />
More accurate forecasts are unlikely to come anytime soon. The problem is that people don't understand the depiction of uncertainty in the graphic models. The focus is on the line that runs down the middle and they ignore the variation about that line, the cone of probability.<br />
<br />
<blockquote>J. Marchall Shepherd, an atmospheric scientist at the University of Georgia exlained the fallacy in a Facebook post. "Anywhere in that cone is a possibility," Dr. Shepherd wrote, "and it has always been a challenge communicating what the cone 'means' versus what people 'think" it means."</blockquote><br />
<br />
===Questions===<br />
<br />
1. There are different maps of the predicted paths of Irma at [https://www.nytimes.com/interactive/2017/09/05/us/hurricane-irma-map.html https://www.nytimes.com/interactive/2017/09/05/us/hurricane-irma-map.html]. One shows a cone of probability and another shows 52 separate predictions of the hurricane's path. Which one better depicts the uncertainty of the prediction?<br />
<br />
2. Dr. Shepherd mentions in a Facebook post referenced b yhe New York Times article that people often confuse the concept of "percent probability of rain." What are some of the potential misinterpretations of this phrase?<br />
<br />
3. Hurricane Harvey reintroduced us to the term "500 year flood." What are some of the potential misinterpretations of this phrase.</div>Simon66217https://www.causeweb.org/wiki/chance/index.php?title=Chance_News_112&diff=21981Chance News 1122017-09-11T18:36:11Z<p>Simon66217: /* Forsooth */</p>
<hr />
<div>'''''Under construction'':''' August 21, 2017 to ...<br />
==Quotations==<br />
<br />
==Forsooth==<br />
"Of the 36 applicants that were interviewed, 20 were ultimately promoted... . Among the promoted individuals, 62 percent were female and 38 percent were male."<br />
<br />
<div align=right> in: [http://www.jdsupra.com/legalnews/woefully-thin-statistics-doom-adverse-41363/ “Woefully thin statistics” doom adverse impact claim], JDSUPRA.com, 24 August 2017</div><br />
<br />
----<br />
The graphic below appears in: [https://www.inverse.com/article/36156-divorce-rate-study-americans-bartenders-flight-attendants New study reveals bartenders, casino workers most likely to get divorced], ''Inverse Culture'', 5 September 2017<br />
[[File:Divorce scatter.png|600px|frameless|center]]<br />
<br />
<br />
(Note: The scatterplot was [http://flowingdata.com/2017/07/25/divorce-and-occupation originally created by ''FlowingData''], where the relationship is correctly<br />
described as "downward slopey"; the caption above is from the ''Inverse Culture'' article.)<br />
----<br />
Facebook advertises that it "can reach 41 million 18 to 24-year-olds in the United States and 60 million 25- to 34-year-olds." But according to the U.S. Census that are only 31 million and 45 million total people in those two demographic groups. Details are in [https://www.nytimes.com/2017/09/06/business/media/facebook-advertisers.html this new York Times article.]<br />
<br />
==Lecture on football probability==<br />
Margaret Cibes sent a link to the following YouTube video:<br />
<br />
:[https://www.youtube.com/watch?v=G5FNHE_EcRA John Urschel-NFL Math Whiz: Real Sports Full Segment (HBO)]<br />
<br />
It features John Urschel, an offensive for the NFL's Baltimore Ravens, who is also studying applied mathematics at MIT. The video begins with John at a chalkboard using a decision tree to analyze a one-point vs. two-point conversion late in a football game.<br />
<br />
John is already a published mathematician, as described in [https://math.mit.edu/~urschel/notices.pdf this 2016 article] from the ''Notices of the AMS''.<br />
<br />
==Redefining statistical significance==<br />
[https://news.uchicago.edu/article/2017/09/01/scholars-take-aim-false-positives-research Scholars take aim at false positives in research]<br><br />
by Thomas Gaulkin, ''UChicagoNews'', 1 September 2017<br />
<br />
University Chicago economist John List is one of 72 collaborators whose commentary, <br />
[https://www.nature.com/articles/s41562-017-0189-z.epdf?author_access_token=Eb6x88zTNQ7PuVxPt1CpXdRgN0jAjWel9jnR3ZoTv0PlqY8PQKtlL9OP0czNSVZ5rodrqWv-lxLd4whdDH-qvHpF5PQtT1U4AblMVaKnbDH0ctY2yThyrB_ccetKNmK4sasDTgzcxT5_u2wTJ8C6sg%3D%3D Redfine statistical significance], was just published in ''Nature Human Behavior''. The subtitle reads, <br />
"We propose to change the default P-value threshold for statistical significance from 0.05 to 0.005 for claims of <br />
new discoveries."<br />
<br />
To be continued...<br />
<br />
==How bad/good were the predictions about Hurricane Irma==<br />
[https://www.nytimes.com/2017/09/10/us/forecast-irma-shift-florida.html Irma Shifting Forecast: It's All a Matter of Probability]. John Schwartz, The New York Times, September 10, 2017.<br />
<br />
How surprising is it that Irma is heading up the "wrong" coast of Florida? Well, it changed the plans of one expert.<br />
<br />
<blockquote>Brian McNoldy, a researcher at the University of Miama and respected blogger on tropical storms and hurricanes, decided on Thursday to evacuate from South Florida with friends and his two dogs and drive to the Tampa area.</blockquote><br />
<br />
Dr. McNoldy ended up travelling back to Miami once the forecast changed, showing Irma smashing into the west coast of Florida rather than hitting Miami dead on.<br />
<br />
Was this a failure of the statistical model? Florida is such a skinny state that Dr. McNoldy admits that predicting where any hurricane will hit is problematic.<br />
<br />
<blockquote>"A hundred miles is the difference between the east coast and the west coast-but a hundred miles in a three-day forecast is really good."</blockquote><br />
<br />
More accurate forecasts are unlikely to come anytime soon. The problem is that people don't understand the depiction of uncertainty in the graphic models. The focus is on the line that runs down the middle and they ignore the variation about that line, the cone of probability.<br />
<br />
<blockquote>J. Marchall Shepherd, an atmospheric scientist at the University of Georgia exlained the fallacy in a Facebook post. "Anywhere in that cone is a possibility," Dr. Shepherd wrote, "and it has always been a challenge communicating what the cone 'means' versus what people 'think" it means."</blockquote><br />
<br />
===Questions===<br />
<br />
1. There are different maps of the predicted paths of Irma at [https://www.nytimes.com/interactive/2017/09/05/us/hurricane-irma-map.html https://www.nytimes.com/interactive/2017/09/05/us/hurricane-irma-map.html]. One shows a cone of probability and another shows 52 separate predictions of the hurricane's path. Which one better depicts the uncertainty of the prediction?<br />
<br />
2. Dr. Shepherd mentions in a Facebook post referenced b yhe New York Times article that people often confuse the concept of "percent probability of rain." What are some of the potential misinterpretations of this phrase?<br />
<br />
3. Hurricane Harvey reintroduced us to the term "500 year flood." What are some of the potential misinterpretations of this phrase.</div>Simon66217https://www.causeweb.org/wiki/chance/index.php?title=Chance_News_112&diff=21980Chance News 1122017-09-10T23:45:16Z<p>Simon66217: /* Questions */</p>
<hr />
<div>'''''Under construction'':''' August 21, 2017 to ...<br />
==Quotations==<br />
<br />
==Forsooth==<br />
"Of the 36 applicants that were interviewed, 20 were ultimately promoted... . Among the promoted individuals, 62 percent were female and 38 percent were male."<br />
<br />
<div align=right> in: [http://www.jdsupra.com/legalnews/woefully-thin-statistics-doom-adverse-41363/ “Woefully thin statistics” doom adverse impact claim], JDSUPRA.com, 24 August 2017</div><br />
<br />
----<br />
The graphic below appears in: [https://www.inverse.com/article/36156-divorce-rate-study-americans-bartenders-flight-attendants New study reveals bartenders, casino workers most likely to get divorced], ''Inverse Culture'', 5 September 2017<br />
[[File:Divorce scatter.png|600px|frameless|center]]<br />
<br />
<br />
(Note: The scatterplot was [http://flowingdata.com/2017/07/25/divorce-and-occupation originally created by ''FlowingData''], where the relationship is correctly<br />
described as "downward slopey"; the caption above is from the ''Inverse Culture'' article.)<br />
<br />
==Lecture on football probability==<br />
Margaret Cibes sent a link to the following YouTube video:<br />
<br />
:[https://www.youtube.com/watch?v=G5FNHE_EcRA John Urschel-NFL Math Whiz: Real Sports Full Segment (HBO)]<br />
<br />
It features John Urschel, an offensive for the NFL's Baltimore Ravens, who is also studying applied mathematics at MIT. The video begins with John at a chalkboard using a decision tree to analyze a one-point vs. two-point conversion late in a football game.<br />
<br />
John is already a published mathematician, as described in [https://math.mit.edu/~urschel/notices.pdf this 2016 article] from the ''Notices of the AMS''.<br />
<br />
==Redefining statistical significance==<br />
[https://news.uchicago.edu/article/2017/09/01/scholars-take-aim-false-positives-research Scholars take aim at false positives in research]<br><br />
by Thomas Gaulkin, ''UChicagoNews'', 1 September 2017<br />
<br />
University Chicago economist John List is one of 72 collaborators whose commentary, <br />
[https://www.nature.com/articles/s41562-017-0189-z.epdf?author_access_token=Eb6x88zTNQ7PuVxPt1CpXdRgN0jAjWel9jnR3ZoTv0PlqY8PQKtlL9OP0czNSVZ5rodrqWv-lxLd4whdDH-qvHpF5PQtT1U4AblMVaKnbDH0ctY2yThyrB_ccetKNmK4sasDTgzcxT5_u2wTJ8C6sg%3D%3D Redfine statistical significance], was just published in ''Nature Human Behavior''. The subtitle reads, <br />
"We propose to change the default P-value threshold for statistical significance from 0.05 to 0.005 for claims of <br />
new discoveries."<br />
<br />
To be continued...<br />
<br />
==How bad/good were the predictions about Hurricane Irma==<br />
[https://www.nytimes.com/2017/09/10/us/forecast-irma-shift-florida.html Irma Shifting Forecast: It's All a Matter of Probability]. John Schwartz, The New York Times, September 10, 2017.<br />
<br />
How surprising is it that Irma is heading up the "wrong" coast of Florida? Well, it changed the plans of one expert.<br />
<br />
<blockquote>Brian McNoldy, a researcher at the University of Miama and respected blogger on tropical storms and hurricanes, decided on Thursday to evacuate from South Florida with friends and his two dogs and drive to the Tampa area.</blockquote><br />
<br />
Dr. McNoldy ended up travelling back to Miami once the forecast changed, showing Irma smashing into the west coast of Florida rather than hitting Miami dead on.<br />
<br />
Was this a failure of the statistical model? Florida is such a skinny state that Dr. McNoldy admits that predicting where any hurricane will hit is problematic.<br />
<br />
<blockquote>"A hundred miles is the difference between the east coast and the west coast-but a hundred miles in a three-day forecast is really good."</blockquote><br />
<br />
More accurate forecasts are unlikely to come anytime soon. The problem is that people don't understand the depiction of uncertainty in the graphic models. The focus is on the line that runs down the middle and they ignore the variation about that line, the cone of probability.<br />
<br />
<blockquote>J. Marchall Shepherd, an atmospheric scientist at the University of Georgia exlained the fallacy in a Facebook post. "Anywhere in that cone is a possibility," Dr. Shepherd wrote, "and it has always been a challenge communicating what the cone 'means' versus what people 'think" it means."</blockquote><br />
<br />
===Questions===<br />
<br />
1. There are different maps of the predicted paths of Irma at [https://www.nytimes.com/interactive/2017/09/05/us/hurricane-irma-map.html https://www.nytimes.com/interactive/2017/09/05/us/hurricane-irma-map.html]. One shows a cone of probability and another shows 52 separate predictions of the hurricane's path. Which one better depicts the uncertainty of the prediction?<br />
<br />
2. Dr. Shepherd mentions in a Facebook post referenced b yhe New York Times article that people often confuse the concept of "percent probability of rain." What are some of the potential misinterpretations of this phrase?<br />
<br />
3. Hurricane Harvey reintroduced us to the term "500 year flood." What are some of the potential misinterpretations of this phrase.</div>Simon66217https://www.causeweb.org/wiki/chance/index.php?title=Chance_News_112&diff=21979Chance News 1122017-09-10T23:44:40Z<p>Simon66217: /* How bad/good were the predictions about Hurricane Irma */</p>
<hr />
<div>'''''Under construction'':''' August 21, 2017 to ...<br />
==Quotations==<br />
<br />
==Forsooth==<br />
"Of the 36 applicants that were interviewed, 20 were ultimately promoted... . Among the promoted individuals, 62 percent were female and 38 percent were male."<br />
<br />
<div align=right> in: [http://www.jdsupra.com/legalnews/woefully-thin-statistics-doom-adverse-41363/ “Woefully thin statistics” doom adverse impact claim], JDSUPRA.com, 24 August 2017</div><br />
<br />
----<br />
The graphic below appears in: [https://www.inverse.com/article/36156-divorce-rate-study-americans-bartenders-flight-attendants New study reveals bartenders, casino workers most likely to get divorced], ''Inverse Culture'', 5 September 2017<br />
[[File:Divorce scatter.png|600px|frameless|center]]<br />
<br />
<br />
(Note: The scatterplot was [http://flowingdata.com/2017/07/25/divorce-and-occupation originally created by ''FlowingData''], where the relationship is correctly<br />
described as "downward slopey"; the caption above is from the ''Inverse Culture'' article.)<br />
<br />
==Lecture on football probability==<br />
Margaret Cibes sent a link to the following YouTube video:<br />
<br />
:[https://www.youtube.com/watch?v=G5FNHE_EcRA John Urschel-NFL Math Whiz: Real Sports Full Segment (HBO)]<br />
<br />
It features John Urschel, an offensive for the NFL's Baltimore Ravens, who is also studying applied mathematics at MIT. The video begins with John at a chalkboard using a decision tree to analyze a one-point vs. two-point conversion late in a football game.<br />
<br />
John is already a published mathematician, as described in [https://math.mit.edu/~urschel/notices.pdf this 2016 article] from the ''Notices of the AMS''.<br />
<br />
==Redefining statistical significance==<br />
[https://news.uchicago.edu/article/2017/09/01/scholars-take-aim-false-positives-research Scholars take aim at false positives in research]<br><br />
by Thomas Gaulkin, ''UChicagoNews'', 1 September 2017<br />
<br />
University Chicago economist John List is one of 72 collaborators whose commentary, <br />
[https://www.nature.com/articles/s41562-017-0189-z.epdf?author_access_token=Eb6x88zTNQ7PuVxPt1CpXdRgN0jAjWel9jnR3ZoTv0PlqY8PQKtlL9OP0czNSVZ5rodrqWv-lxLd4whdDH-qvHpF5PQtT1U4AblMVaKnbDH0ctY2yThyrB_ccetKNmK4sasDTgzcxT5_u2wTJ8C6sg%3D%3D Redfine statistical significance], was just published in ''Nature Human Behavior''. The subtitle reads, <br />
"We propose to change the default P-value threshold for statistical significance from 0.05 to 0.005 for claims of <br />
new discoveries."<br />
<br />
To be continued...<br />
<br />
==How bad/good were the predictions about Hurricane Irma==<br />
[https://www.nytimes.com/2017/09/10/us/forecast-irma-shift-florida.html Irma Shifting Forecast: It's All a Matter of Probability]. John Schwartz, The New York Times, September 10, 2017.<br />
<br />
How surprising is it that Irma is heading up the "wrong" coast of Florida? Well, it changed the plans of one expert.<br />
<br />
<blockquote>Brian McNoldy, a researcher at the University of Miama and respected blogger on tropical storms and hurricanes, decided on Thursday to evacuate from South Florida with friends and his two dogs and drive to the Tampa area.</blockquote><br />
<br />
Dr. McNoldy ended up travelling back to Miami once the forecast changed, showing Irma smashing into the west coast of Florida rather than hitting Miami dead on.<br />
<br />
Was this a failure of the statistical model? Florida is such a skinny state that Dr. McNoldy admits that predicting where any hurricane will hit is problematic.<br />
<br />
<blockquote>"A hundred miles is the difference between the east coast and the west coast-but a hundred miles in a three-day forecast is really good."</blockquote><br />
<br />
More accurate forecasts are unlikely to come anytime soon. The problem is that people don't understand the depiction of uncertainty in the graphic models. The focus is on the line that runs down the middle and they ignore the variation about that line, the cone of probability.<br />
<br />
<blockquote>J. Marchall Shepherd, an atmospheric scientist at the University of Georgia exlained the fallacy in a Facebook post. "Anywhere in that cone is a possibility," Dr. Shepherd wrote, "and it has always been a challenge communicating what the cone 'means' versus what people 'think" it means."</blockquote><br />
<br />
===Questions===<br />
<br />
1. There are different maps of the predicted paths of Irma at [https://www.nytimes.com/interactive/2017/09/05/us/hurricane-irma-map.html https://www.nytimes.com/interactive/2017/09/05/us/hurricane-irma-map.html]. One shows a cone of probability and another shows 52 separate predictions of the hurricane's path. Which one better depicts the uncertainty of the prediction?<br />
<br />
2. Dr. Shepherd mentions in a Facebook post referenced b yhe New York Times article that people often confuse the concept of "percent probability of rain." What are some of the potential misinterpretations of this phrase?<br />
<br />
3. Hurricane Harvey introduced us to the term "500 year flood." What are some of the potential misinterpretations of this phrase.</div>Simon66217https://www.causeweb.org/wiki/chance/index.php?title=Chance_News_112&diff=21978Chance News 1122017-09-10T23:24:02Z<p>Simon66217: /* How bad/good were the predictions about Hurricane Irma */</p>
<hr />
<div>'''''Under construction'':''' August 21, 2017 to ...<br />
==Quotations==<br />
<br />
==Forsooth==<br />
"Of the 36 applicants that were interviewed, 20 were ultimately promoted... . Among the promoted individuals, 62 percent were female and 38 percent were male."<br />
<br />
<div align=right> in: [http://www.jdsupra.com/legalnews/woefully-thin-statistics-doom-adverse-41363/ “Woefully thin statistics” doom adverse impact claim], JDSUPRA.com, 24 August 2017</div><br />
<br />
----<br />
The graphic below appears in: [https://www.inverse.com/article/36156-divorce-rate-study-americans-bartenders-flight-attendants New study reveals bartenders, casino workers most likely to get divorced], ''Inverse Culture'', 5 September 2017<br />
[[File:Divorce scatter.png|600px|frameless|center]]<br />
<br />
<br />
(Note: The scatterplot was [http://flowingdata.com/2017/07/25/divorce-and-occupation originally created by ''FlowingData''], where the relationship is correctly<br />
described as "downward slopey"; the caption above is from the ''Inverse Culture'' article.)<br />
<br />
==Lecture on football probability==<br />
Margaret Cibes sent a link to the following YouTube video:<br />
<br />
:[https://www.youtube.com/watch?v=G5FNHE_EcRA John Urschel-NFL Math Whiz: Real Sports Full Segment (HBO)]<br />
<br />
It features John Urschel, an offensive for the NFL's Baltimore Ravens, who is also studying applied mathematics at MIT. The video begins with John at a chalkboard using a decision tree to analyze a one-point vs. two-point conversion late in a football game.<br />
<br />
John is already a published mathematician, as described in [https://math.mit.edu/~urschel/notices.pdf this 2016 article] from the ''Notices of the AMS''.<br />
<br />
==Redefining statistical significance==<br />
[https://news.uchicago.edu/article/2017/09/01/scholars-take-aim-false-positives-research Scholars take aim at false positives in research]<br><br />
by Thomas Gaulkin, ''UChicagoNews'', 1 September 2017<br />
<br />
University Chicago economist John List is one of 72 collaborators whose commentary, <br />
[https://www.nature.com/articles/s41562-017-0189-z.epdf?author_access_token=Eb6x88zTNQ7PuVxPt1CpXdRgN0jAjWel9jnR3ZoTv0PlqY8PQKtlL9OP0czNSVZ5rodrqWv-lxLd4whdDH-qvHpF5PQtT1U4AblMVaKnbDH0ctY2yThyrB_ccetKNmK4sasDTgzcxT5_u2wTJ8C6sg%3D%3D Redfine statistical significance], was just published in ''Nature Human Behavior''. The subtitle reads, <br />
"We propose to change the default P-value threshold for statistical significance from 0.05 to 0.005 for claims of <br />
new discoveries."<br />
<br />
To be continued...<br />
<br />
==How bad/good were the predictions about Hurricane Irma==<br />
[https://www.nytimes.com/2017/09/10/us/forecast-irma-shift-florida.html Irma Shifting Forecast: It's All a Matter of Probability]. John Schwartz, The New York Times, September 10, 2017.<br />
<br />
How surprising is it that Irma is heading up the "wrong" coast of Florida? Well, it changed the plans of one expert.<br />
<br />
<blockquote>Brian McNoldy, a researcher at the University of Miama and respected blogger on tropical storms and hurricanes, decided on Thursday to evacuate from South Florida with friends and his two dogs and drive to the Tampa area.</blockquote><br />
<br />
Dr. McNoldy ended up travelling back to Miami once the forecast changed, showing Irma smashing into the west coast of Florida rather than hitting Miami dead on.<br />
<br />
Was this a failure of the statistical model? Florida is such a skinny state that Dr. McNoldy admits that predicting where any hurricane will hit is problematic.<br />
<br />
<blockquote>"A hundred miles is the difference between the east coast and the west coast-but a hundred miles in a three-day forecast is really good."<\blockquote></div>Simon66217https://www.causeweb.org/wiki/chance/index.php?title=Chance_News_112&diff=21977Chance News 1122017-09-10T23:16:05Z<p>Simon66217: /* Redefining statistical significance */</p>
<hr />
<div>'''''Under construction'':''' August 21, 2017 to ...<br />
==Quotations==<br />
<br />
==Forsooth==<br />
"Of the 36 applicants that were interviewed, 20 were ultimately promoted... . Among the promoted individuals, 62 percent were female and 38 percent were male."<br />
<br />
<div align=right> in: [http://www.jdsupra.com/legalnews/woefully-thin-statistics-doom-adverse-41363/ “Woefully thin statistics” doom adverse impact claim], JDSUPRA.com, 24 August 2017</div><br />
<br />
----<br />
The graphic below appears in: [https://www.inverse.com/article/36156-divorce-rate-study-americans-bartenders-flight-attendants New study reveals bartenders, casino workers most likely to get divorced], ''Inverse Culture'', 5 September 2017<br />
[[File:Divorce scatter.png|600px|frameless|center]]<br />
<br />
<br />
(Note: The scatterplot was [http://flowingdata.com/2017/07/25/divorce-and-occupation originally created by ''FlowingData''], where the relationship is correctly<br />
described as "downward slopey"; the caption above is from the ''Inverse Culture'' article.)<br />
<br />
==Lecture on football probability==<br />
Margaret Cibes sent a link to the following YouTube video:<br />
<br />
:[https://www.youtube.com/watch?v=G5FNHE_EcRA John Urschel-NFL Math Whiz: Real Sports Full Segment (HBO)]<br />
<br />
It features John Urschel, an offensive for the NFL's Baltimore Ravens, who is also studying applied mathematics at MIT. The video begins with John at a chalkboard using a decision tree to analyze a one-point vs. two-point conversion late in a football game.<br />
<br />
John is already a published mathematician, as described in [https://math.mit.edu/~urschel/notices.pdf this 2016 article] from the ''Notices of the AMS''.<br />
<br />
==Redefining statistical significance==<br />
[https://news.uchicago.edu/article/2017/09/01/scholars-take-aim-false-positives-research Scholars take aim at false positives in research]<br><br />
by Thomas Gaulkin, ''UChicagoNews'', 1 September 2017<br />
<br />
University Chicago economist John List is one of 72 collaborators whose commentary, <br />
[https://www.nature.com/articles/s41562-017-0189-z.epdf?author_access_token=Eb6x88zTNQ7PuVxPt1CpXdRgN0jAjWel9jnR3ZoTv0PlqY8PQKtlL9OP0czNSVZ5rodrqWv-lxLd4whdDH-qvHpF5PQtT1U4AblMVaKnbDH0ctY2yThyrB_ccetKNmK4sasDTgzcxT5_u2wTJ8C6sg%3D%3D Redfine statistical significance], was just published in ''Nature Human Behavior''. The subtitle reads, <br />
"We propose to change the default P-value threshold for statistical significance from 0.05 to 0.005 for claims of <br />
new discoveries."<br />
<br />
To be continued...<br />
<br />
==How bad/good were the predictions about Hurricane Irma==<br />
[https://www.nytimes.com/2017/09/10/us/forecast-irma-shift-florida.html Irma Shifting Forecast: It's All a Matter of Probability]<br />
<br />
How surprising is it that Irma is heading up the "wrong" coast of Florida? Well, it changed the plans of one expert.<br />
<br />
<blockquote>Brian McNoldy, a researcher at the University of Miama and respected blogger on tropical storms and hurricanes, decided on Thursday to evacuate from South Florida with friends and his two dogs and drive to the Tampa area.</blockquote></div>Simon66217https://www.causeweb.org/wiki/chance/index.php?title=Chance_News_111&diff=21588Chance News 1112017-07-07T18:39:05Z<p>Simon66217: /* Sense and Sensibility and Statistics */</p>
<hr />
<div>July 1, 2017 to ...<br />
==Quotations==<br />
“In my darkest moods I follow what could be called the ‘Groucho principle’: <br />
because stories have gone through so many filters that encourage distortion and selection, <br />
the very fact that I am hearing a claim based on statistics is reason to disbelieve it.”<br />
<br />
<div align=right> -- David Spiegelhalter (president of Royal Statistical Society), quoted in:<br><br />
[https://www.theguardian.com/science/2017/jun/28/exaggerations-threaten-public-trust-in-science-leading-statistician-david-spiegelhalter 'Exaggerations' threaten public trust in science, says leading statistician], ''Guardian'', 28 June 2017</div><br />
<br />
----<br />
"Aside from ruining the rest of the 21st century, the 2016 U.S. election inflicted spectacular collateral damage on the subject known as statistics."<br />
<br />
<div align=right>-- Paul Alper, in: Occom, Mencken and Cohn, ''Higher Education Review'', vol. 49, no.2 </div><br />
<br />
==Forsooth==<br />
<br />
=="Random" seat assignment==<br />
[http://www.independent.ie/irish-news/ryanairs-random-seat-allocation-not-random-scientists-35880337.html Ryanair's 'random' seat allocation not random - scientists]<br><br />
by John von Radowitz, ''Irish Independent'', 30 June 2017 <br />
<br />
Ryanair is an Irish low-cost airline. This [https://en.wikipedia.org/wiki/Ryanair Wikipedia entry] reports<br />
that they have repeatedly faced criticism for misleading advertising.<br />
<br />
The ''Irish Independent'' article concerns a recent controversy. When customers book Ryanair flights, they can pay to make a seat selection<br />
or else opt for "random" seat assignment, which is free.<br />
Advertising on the airline's website says, "Can't stand the middle seat? Don't leave it<br />
to chance, take your pick from a choice of seats. Get up to 50pc off<br />
reserved seats with prices starting at £2."<br />
<br />
But is it up to chance? In light of [http://www.bbc.co.uk/programmes/articles/1JB3yVCzRWB6HqG7tgfYj4X/ryanair customer complaints], the BBC consumer affairs show ''Watchdog'' sought expert opinion from Oxford University. To test the claim, researchers had four groups of four passengers book travel on four<br />
separate flights, all under the random seating option. On every flight, all of the passengers got middle seats. The odds of this happening <br />
were estimated at about 1:540,000,000. Compare this to the 1:45,000,000 odds of <br />
[http://www.murderousmaths.co.uk/books/bkmm6xlo.htm winning the UK National Lottery] jackpot. The director of Oxford University's Statistical<br />
Consultancy, Dr. Jennifer Rogers, is quoted as saying, "This is a highly controversial topic and my analysis<br />
cast doubt on whether Ryanair's seat allocation can be purely random."<br />
<br />
The article concludes with the following explanation from Ryanair, which qualifies as an extended Forsooth!<br />
<blockquote><br />
We haven't changed the random seat<br />
allocation policy.<br />
<br><br><br />
The reason for more middle seats being allocated is that more and more<br />
passengers are taking our reserved seats (from just £2) and these<br />
passengers overwhelmingly prefer aisle and window seats which is why<br />
people who choose random (free of charge) seats are more likely to be<br />
allocated middle seats.<br />
<br><br><br />
Some random seat passengers are confused by the appearance of empty<br />
seats beside them when they check-in up to four days prior to departure.<br />
<br><br><br />
The reason they can't have these window or aisle seats is that these<br />
are more likely to be selected by reserved seat passengers, many of whom<br />
only check in 24 hours prior to departure.<br />
<br><br><br />
Since our current load factor is 95pc, we have to keep these window and<br />
aisle seats free to facilitate those customers who are willing to pay<br />
(from £2) for them.<br />
</blockquote><br />
<br />
Submitted by Patrick O'Beirne<br />
<br />
==Sense and Sensibility and Statistics==<br />
<br />
From [https://www.nytimes.com/2017/07/06/upshot/the-word-choices-that-explain-why-jane-austen-endures.html The Word Choices That Explain Why Jane Austen Endures] by Kathleen A. Flynn and Josh Katz. The New York times, July 6, 2017.<br />
<br />
Jane Austen' popularity has endured, and there may be something in her language that explains this. A principal components analysis of the words used by a large number books published from 1701 t0 1920 show that Austen's novels were unusual in her use of words related to time (always, fortnight and week) or emotion (awkward, decided, dislike, glad, sorry, suppose).<br />
<br />
Jane Austen also uses a large number of intensifying words: quite, really, and very. These words are normally avoided by authors, but Austen uses them to develop a sense of irony. This fits in well with some non-statistical assessments.<br />
<br />
<blockquote>Traditional literary approaches to Austen have long focused on this aspect of her work: “the incongruities between pretence and essence, between the large idea and the inadequate ego," as the critic Marvin Mudrick put it. A look at passages where words like very are used frequently often finds the stated meaning conceivably at odds with the real one, the exaggeration subtly inviting doubt."</blockquote><br />
<br />
A nice interactive plot shows the first two principal components and you can hover over individual data points to see the book title.<br />
<br />
Submitted by Steve Simon<br />
<br />
==Item #2==</div>Simon66217https://www.causeweb.org/wiki/chance/index.php?title=Chance_News_111&diff=21587Chance News 1112017-07-07T18:38:22Z<p>Simon66217: /* Sense and Sensibility and Statistics */</p>
<hr />
<div>July 1, 2017 to ...<br />
==Quotations==<br />
“In my darkest moods I follow what could be called the ‘Groucho principle’: <br />
because stories have gone through so many filters that encourage distortion and selection, <br />
the very fact that I am hearing a claim based on statistics is reason to disbelieve it.”<br />
<br />
<div align=right> -- David Spiegelhalter (president of Royal Statistical Society), quoted in:<br><br />
[https://www.theguardian.com/science/2017/jun/28/exaggerations-threaten-public-trust-in-science-leading-statistician-david-spiegelhalter 'Exaggerations' threaten public trust in science, says leading statistician], ''Guardian'', 28 June 2017</div><br />
<br />
----<br />
"Aside from ruining the rest of the 21st century, the 2016 U.S. election inflicted spectacular collateral damage on the subject known as statistics."<br />
<br />
<div align=right>-- Paul Alper, in: Occom, Mencken and Cohn, ''Higher Education Review'', vol. 49, no.2 </div><br />
<br />
==Forsooth==<br />
<br />
=="Random" seat assignment==<br />
[http://www.independent.ie/irish-news/ryanairs-random-seat-allocation-not-random-scientists-35880337.html Ryanair's 'random' seat allocation not random - scientists]<br><br />
by John von Radowitz, ''Irish Independent'', 30 June 2017 <br />
<br />
Ryanair is an Irish low-cost airline. This [https://en.wikipedia.org/wiki/Ryanair Wikipedia entry] reports<br />
that they have repeatedly faced criticism for misleading advertising.<br />
<br />
The ''Irish Independent'' article concerns a recent controversy. When customers book Ryanair flights, they can pay to make a seat selection<br />
or else opt for "random" seat assignment, which is free.<br />
Advertising on the airline's website says, "Can't stand the middle seat? Don't leave it<br />
to chance, take your pick from a choice of seats. Get up to 50pc off<br />
reserved seats with prices starting at £2."<br />
<br />
But is it up to chance? In light of [http://www.bbc.co.uk/programmes/articles/1JB3yVCzRWB6HqG7tgfYj4X/ryanair customer complaints], the BBC consumer affairs show ''Watchdog'' sought expert opinion from Oxford University. To test the claim, researchers had four groups of four passengers book travel on four<br />
separate flights, all under the random seating option. On every flight, all of the passengers got middle seats. The odds of this happening <br />
were estimated at about 1:540,000,000. Compare this to the 1:45,000,000 odds of <br />
[http://www.murderousmaths.co.uk/books/bkmm6xlo.htm winning the UK National Lottery] jackpot. The director of Oxford University's Statistical<br />
Consultancy, Dr. Jennifer Rogers, is quoted as saying, "This is a highly controversial topic and my analysis<br />
cast doubt on whether Ryanair's seat allocation can be purely random."<br />
<br />
The article concludes with the following explanation from Ryanair, which qualifies as an extended Forsooth!<br />
<blockquote><br />
We haven't changed the random seat<br />
allocation policy.<br />
<br><br><br />
The reason for more middle seats being allocated is that more and more<br />
passengers are taking our reserved seats (from just £2) and these<br />
passengers overwhelmingly prefer aisle and window seats which is why<br />
people who choose random (free of charge) seats are more likely to be<br />
allocated middle seats.<br />
<br><br><br />
Some random seat passengers are confused by the appearance of empty<br />
seats beside them when they check-in up to four days prior to departure.<br />
<br><br><br />
The reason they can't have these window or aisle seats is that these<br />
are more likely to be selected by reserved seat passengers, many of whom<br />
only check in 24 hours prior to departure.<br />
<br><br><br />
Since our current load factor is 95pc, we have to keep these window and<br />
aisle seats free to facilitate those customers who are willing to pay<br />
(from £2) for them.<br />
</blockquote><br />
<br />
Submitted by Patrick O'Beirne<br />
<br />
==Sense and Sensibility and Statistics==<br />
<br />
From [https://www.nytimes.com/2017/07/06/upshot/the-word-choices-that-explain-why-jane-austen-endures.html The Word Choices That Explain Why Jane Austen Endures] by Kathleen A. Flynn and Josh Katz. The New York times, July 6, 2017.<br />
<br />
Jane Austen' popularity has endured, and there may be something in her language that explains this. A principal components analysis of the words used by a large number books published from 1701 t0 1920 show that Austen's novels were unusual in her use of words related to time (always, fortnight and week) or emotion (awkward, decided, dislike, glad, sorry, suppose).<br />
<br />
Jane Austen also uses a large number of intensifying words: quite, really, and very. These words are normally avoided by authors, but Austen uses them to develop a sense of irony. This fits in well with some non-statistical assessments.<br />
<br />
<blockquote>Traditional literary approaches to Austen have long focused on this aspect of her work: “the incongruities between pretence and essence, between the large idea and the inadequate ego," as the critic Marvin Mudrick put it. A look at passages where words like very are used frequently often finds the stated meaning conceivably at odds with the real one, the exaggeration subtly inviting doubt."</blockquote><br />
<br />
A nice interactive plot shows the first two principal components and you can hover over individual data points to see the book title.<br />
<br />
==Item #2==</div>Simon66217https://www.causeweb.org/wiki/chance/index.php?title=Chance_News_111&diff=21586Chance News 1112017-07-07T18:34:17Z<p>Simon66217: /* Sense and Sensibility and Statistics */</p>
<hr />
<div>July 1, 2017 to ...<br />
==Quotations==<br />
“In my darkest moods I follow what could be called the ‘Groucho principle’: <br />
because stories have gone through so many filters that encourage distortion and selection, <br />
the very fact that I am hearing a claim based on statistics is reason to disbelieve it.”<br />
<br />
<div align=right> -- David Spiegelhalter (president of Royal Statistical Society), quoted in:<br><br />
[https://www.theguardian.com/science/2017/jun/28/exaggerations-threaten-public-trust-in-science-leading-statistician-david-spiegelhalter 'Exaggerations' threaten public trust in science, says leading statistician], ''Guardian'', 28 June 2017</div><br />
<br />
----<br />
"Aside from ruining the rest of the 21st century, the 2016 U.S. election inflicted spectacular collateral damage on the subject known as statistics."<br />
<br />
<div align=right>-- Paul Alper, in: Occom, Mencken and Cohn, ''Higher Education Review'', vol. 49, no.2 </div><br />
<br />
==Forsooth==<br />
<br />
=="Random" seat assignment==<br />
[http://www.independent.ie/irish-news/ryanairs-random-seat-allocation-not-random-scientists-35880337.html Ryanair's 'random' seat allocation not random - scientists]<br><br />
by John von Radowitz, ''Irish Independent'', 30 June 2017 <br />
<br />
Ryanair is an Irish low-cost airline. This [https://en.wikipedia.org/wiki/Ryanair Wikipedia entry] reports<br />
that they have repeatedly faced criticism for misleading advertising.<br />
<br />
The ''Irish Independent'' article concerns a recent controversy. When customers book Ryanair flights, they can pay to make a seat selection<br />
or else opt for "random" seat assignment, which is free.<br />
Advertising on the airline's website says, "Can't stand the middle seat? Don't leave it<br />
to chance, take your pick from a choice of seats. Get up to 50pc off<br />
reserved seats with prices starting at £2."<br />
<br />
But is it up to chance? In light of [http://www.bbc.co.uk/programmes/articles/1JB3yVCzRWB6HqG7tgfYj4X/ryanair customer complaints], the BBC consumer affairs show ''Watchdog'' sought expert opinion from Oxford University. To test the claim, researchers had four groups of four passengers book travel on four<br />
separate flights, all under the random seating option. On every flight, all of the passengers got middle seats. The odds of this happening <br />
were estimated at about 1:540,000,000. Compare this to the 1:45,000,000 odds of <br />
[http://www.murderousmaths.co.uk/books/bkmm6xlo.htm winning the UK National Lottery] jackpot. The director of Oxford University's Statistical<br />
Consultancy, Dr. Jennifer Rogers, is quoted as saying, "This is a highly controversial topic and my analysis<br />
cast doubt on whether Ryanair's seat allocation can be purely random."<br />
<br />
The article concludes with the following explanation from Ryanair, which qualifies as an extended Forsooth!<br />
<blockquote><br />
We haven't changed the random seat<br />
allocation policy.<br />
<br><br><br />
The reason for more middle seats being allocated is that more and more<br />
passengers are taking our reserved seats (from just £2) and these<br />
passengers overwhelmingly prefer aisle and window seats which is why<br />
people who choose random (free of charge) seats are more likely to be<br />
allocated middle seats.<br />
<br><br><br />
Some random seat passengers are confused by the appearance of empty<br />
seats beside them when they check-in up to four days prior to departure.<br />
<br><br><br />
The reason they can't have these window or aisle seats is that these<br />
are more likely to be selected by reserved seat passengers, many of whom<br />
only check in 24 hours prior to departure.<br />
<br><br><br />
Since our current load factor is 95pc, we have to keep these window and<br />
aisle seats free to facilitate those customers who are willing to pay<br />
(from £2) for them.<br />
</blockquote><br />
<br />
Submitted by Patrick O'Beirne<br />
<br />
==Sense and Sensibility and Statistics==<br />
<br />
From [https://www.nytimes.com/2017/07/06/upshot/the-word-choices-that-explain-why-jane-austen-endures.html The Word Choices That Explain Why Jane Austen Endures] by Kathleen A. Flynn and Josh Katz.<br />
<br />
Jane Austen' popularity has endured, and there may be something in her language that explains this. A principal components analysis of the words used by a large number books published from 1701 t0 1920 show that Austen's novels were unusual in her use of words related to emotion or time: always, fortnight and week; awkward, decided, dislike, glad, sorry, suppose.<br />
<br />
Jane Austen also uses a large number of intensifying words: quite, really, and very. These words are normally avoided by authors, but Austen uses them to develop a sense of irony. This fits in well with some non-statistical assessments.<br />
<br />
<blockquote>Traditional literary approaches to Austen have long focused on this aspect of her work: “the incongruities between pretence and essence, between the large idea and the inadequate ego," as the critic Marvin Mudrick put it. A look at passages where words like very are used frequently often finds the stated meaning conceivably at odds with the real one, the exaggeration subtly inviting doubt."</blockquote><br />
<br />
==Item #2==</div>Simon66217https://www.causeweb.org/wiki/chance/index.php?title=Chance_News_111&diff=21585Chance News 1112017-07-07T18:33:37Z<p>Simon66217: /* Item #2 */</p>
<hr />
<div>July 1, 2017 to ...<br />
==Quotations==<br />
“In my darkest moods I follow what could be called the ‘Groucho principle’: <br />
because stories have gone through so many filters that encourage distortion and selection, <br />
the very fact that I am hearing a claim based on statistics is reason to disbelieve it.”<br />
<br />
<div align=right> -- David Spiegelhalter (president of Royal Statistical Society), quoted in:<br><br />
[https://www.theguardian.com/science/2017/jun/28/exaggerations-threaten-public-trust-in-science-leading-statistician-david-spiegelhalter 'Exaggerations' threaten public trust in science, says leading statistician], ''Guardian'', 28 June 2017</div><br />
<br />
----<br />
"Aside from ruining the rest of the 21st century, the 2016 U.S. election inflicted spectacular collateral damage on the subject known as statistics."<br />
<br />
<div align=right>-- Paul Alper, in: Occom, Mencken and Cohn, ''Higher Education Review'', vol. 49, no.2 </div><br />
<br />
==Forsooth==<br />
<br />
=="Random" seat assignment==<br />
[http://www.independent.ie/irish-news/ryanairs-random-seat-allocation-not-random-scientists-35880337.html Ryanair's 'random' seat allocation not random - scientists]<br><br />
by John von Radowitz, ''Irish Independent'', 30 June 2017 <br />
<br />
Ryanair is an Irish low-cost airline. This [https://en.wikipedia.org/wiki/Ryanair Wikipedia entry] reports<br />
that they have repeatedly faced criticism for misleading advertising.<br />
<br />
The ''Irish Independent'' article concerns a recent controversy. When customers book Ryanair flights, they can pay to make a seat selection<br />
or else opt for "random" seat assignment, which is free.<br />
Advertising on the airline's website says, "Can't stand the middle seat? Don't leave it<br />
to chance, take your pick from a choice of seats. Get up to 50pc off<br />
reserved seats with prices starting at £2."<br />
<br />
But is it up to chance? In light of [http://www.bbc.co.uk/programmes/articles/1JB3yVCzRWB6HqG7tgfYj4X/ryanair customer complaints], the BBC consumer affairs show ''Watchdog'' sought expert opinion from Oxford University. To test the claim, researchers had four groups of four passengers book travel on four<br />
separate flights, all under the random seating option. On every flight, all of the passengers got middle seats. The odds of this happening <br />
were estimated at about 1:540,000,000. Compare this to the 1:45,000,000 odds of <br />
[http://www.murderousmaths.co.uk/books/bkmm6xlo.htm winning the UK National Lottery] jackpot. The director of Oxford University's Statistical<br />
Consultancy, Dr. Jennifer Rogers, is quoted as saying, "This is a highly controversial topic and my analysis<br />
cast doubt on whether Ryanair's seat allocation can be purely random."<br />
<br />
The article concludes with the following explanation from Ryanair, which qualifies as an extended Forsooth!<br />
<blockquote><br />
We haven't changed the random seat<br />
allocation policy.<br />
<br><br><br />
The reason for more middle seats being allocated is that more and more<br />
passengers are taking our reserved seats (from just £2) and these<br />
passengers overwhelmingly prefer aisle and window seats which is why<br />
people who choose random (free of charge) seats are more likely to be<br />
allocated middle seats.<br />
<br><br><br />
Some random seat passengers are confused by the appearance of empty<br />
seats beside them when they check-in up to four days prior to departure.<br />
<br><br><br />
The reason they can't have these window or aisle seats is that these<br />
are more likely to be selected by reserved seat passengers, many of whom<br />
only check in 24 hours prior to departure.<br />
<br><br><br />
Since our current load factor is 95pc, we have to keep these window and<br />
aisle seats free to facilitate those customers who are willing to pay<br />
(from £2) for them.<br />
</blockquote><br />
<br />
Submitted by Patrick O'Beirne<br />
<br />
==Sense and Sensibility and Statistics==<br />
<br />
From [https://www.nytimes.com/2017/07/06/upshot/the-word-choices-that-explain-why-jane-austen-endures.html The Word Choices That Explain Why Jane Austen Endures] by Kathleen A. Flynn and Josh Katz.<br />
<br />
Jane Austen' popularity has endured, and there may be something in her language that explains this. A principal components analysis of the words used by a large number books published from 1701 t0 1920 show that Austen's novels were unusual in her use of words related to emotion or time: always, fortnight and week; awkward, decided, dislike, glad, sorry, suppose.<br />
<br />
Jane Austen also uses a large number of intensifying words: quite, really, and very. These words are normally avoided by authors, but Austen uses them to develop a sense of irony. This fits in well with some non-statistical assessments.<br />
<br />
<blockquote>Traditional literary approaches to Austen have long focused on this aspect of her work: “the incongruities between pretence and essence, between the large idea and the inadequate ego," as the critic Marvin Mudrick put it. A look at passages where words like very are used frequently often finds the stated meaning conceivably at odds with the real one, the exaggeration subtly inviting doubt."<br />
<br />
<br />
<br />
==Item #2==</div>Simon66217https://www.causeweb.org/wiki/chance/index.php?title=Chance_News_94&diff=17721Chance News 942013-07-05T20:06:34Z<p>Simon66217: /* Twitter settles the argument about geeks versus nerds */</p>
<hr />
<div>==Quotations==<br />
"...classic PPT statistical graphic: 13 logos, 10 numbers, 9 bubbles, 1 giant green arrow."<br />
<br />
<div align=right> -- Edward Tufte, [https://twitter.com/EdwardTufte/status/342819681054375936/photo/1 tweeting] about the NSA's presentation on its controversial data-collection activities.</div><br />
<br />
[quoted in: [http://www.washingtonpost.com/blogs/wonkblog/wp/2013/06/07/the-real-nsa-scandal-the-horrible-slides/ The real NSA scandal? The horrible slides]. ''Washington Post'', Wonkblog, 7 June 2013.]<br />
<br />
Submitted by Bill Peterson<br />
----<br />
“6. (Mar's Law) Everything is linear if plotted log-log with a fat magic marker.”<br />
<div align=right>from: [http://spacecraft.ssl.umd.edu/old_site/academics/akins_laws.html Akin's Laws of Spacecraft Design]</div><br />
<br />
Submitted by Margaret Cibes<br />
<br />
----<br />
According to [http://en.wikipedia.org/wiki/Derek_J._de_Solla_Price Derek de Solla Price], "...in order to relieve the tedium of sitting for a portrait painter, on two different occasions he [Francis Galton] computed the number of brush strokes and found about 20,000 to the portrait; just the same number, he calculated, as the hand movements that went into the knitting of a pair of socks."<br />
<br />
<div align=right> in: [http://www.kauffman.org/research-and-policy/the-half-life-of-facts.aspx ''The Half-Life of Facts''] by Samuel Arbesman, p. 167</div><br />
<br />
Submitted by Paul Alper<br />
<br />
----<br />
"Statistics is the science that lets you do twenty experiments a year and publish one false result in ''Nature''."<br />
<br />
<div align=right> -- John Maynard Smith ([http://en.wikipedia.org/wiki/John_Maynard_Smith British evolutionary biologist]), quoted in: ''The Half-Life of Facts'', p. 154</div><br />
<br />
Submitted by Paul Alper<br />
<br />
==Forsooth==<br />
“The afternoon wave of [U.S. Open] starters began their first round Thursday in hot, sticky conditions and finished in cool, breezy weather on Friday. Luke Donald described the difference as ‘180 degrees’ ….”<br />
<div align=right>[http://sports.blogs.nytimes.com/2013/06/14/live-updates-follow-fridays-action-at-the-u-s-open/?hp “Mickelson Tied With Horschel for U.S. Open Lead”]<br><br />
by Karen Crouse, <i>The New York Times</i>, June 14, 2013</div><br />
Submitted by Margaret Cibes at the suggestion of Jim Greenwood<br><br />
<br />
----<br />
"Doctors were on board and volunteered to help in 48 percent of cases; nurses and other health workers were available in another 28 percent. Only one-third of cases had to be handled by flight attendants alone."<br />
<div align=right>in: [http://www.heraldnet.com/article/20130530/NEWS02/705309903 50-50 chance of a doctor on board a flight.] ''Herald.Net'', 30 May 2013</div><br />
<br />
Submitted by Bill Peterson<br />
<br />
----<br />
"So for a company that thought there was a 60 percent chance that it would have to pay $1,000 on a claim, and a 40 percent chance it would have to pay $2,000, its required reserve would rise from $1,000, the most probable number, to $1,400 — the average of the probabilities."<br />
<div align=right>in: [http://www.nytimes.com/2013/06/27/business/new-rules-expected-for-insurance-accounting-may-lead-to-erratic-earnings.html New rules expected for insurance accounting may lead to erratic earnings], ''New York Times'', 27 June 2013</div><br />
<br />
Submitted by Bill Peterson<br />
<br />
==Statistics Without Borders==<br />
[http://community.amstat.org/StatisticsWithoutBorders/Home/ Statistics Without Borders]<br><br />
(not to be confused with “<u>Statisticians WithOut</u> Borders), a consulting group)<br><br />
<br />
Current or future statisticians may be interested in the all-volunteer organization Statistics Without Borders. SWB is an Outreach Group of the ASA consisting of over 400 volunteer statisticians who provide free statistical consulting to organizations and government agencies, particularly from developing nations. Its goal is to “promote the use of statistics to improve the health and well-being of all people.”<br> <br />
<br />
The April 2013 issue of <i>Significance</i> magazine contains an article, “Haiti after the earthquake,”[http://www.significancemagazine.org/details/magazine/4705871/Haiti-after-the-earthquake-Statistics-Without-Borders.html] that describes one of their 2010 projects.<br><br />
<br />
Submitted by Margaret Cibes<br />
<br />
==Data about cell and landline phone usage ==<br />
[http://www.nationaljournal.com/blogs/hotlineoncall/2013/06/researchers-warn-of-bias-in-landline-only-phone-polls-18 “Researchers Warn of ‘Bias’ in Landline-Only Phone Polls”]<br><br />
by Steven Shepard, <i>National Journal</i>, June 18, 2013<br><br />
<br />
The CDC reports that landline phone surveys in 2012 were most likely to reach older, whiter Americans. The article gives a number of statistics about landline vs. cell phone usage among various demographic groups. <br />
<br />
(The article also states that it is illegal for automatic dialers to call cell phones, which makes polling cell phone holders more expensive. I’m not sure how many companies observe this ban!)<br />
<br />
One interesting fact, if true: <br />
<blockquote>Calling the proper number of cell phones is not a guarantee of accuracy: Gallup, which called the most cell phones, was considered among the least accurate survey firms in its 2012 pre-election polls; PPP, which called none, was considered among the most accurate.</blockquote><br />
Submitted by Margaret Cibes<br />
<br />
==Google's hiring methodology==<br />
Paul Alper sent a link to the following:<br />
<br />
[http://www.linkedin.com/today/post/article/20130620142512-35894743-on-gpas-and-brain-teasers-new-insights-from-google-on-recruiting-and-hiring On GPAs and brainteasers: New insights from Google on recruiting and hiring], by Adam Bryant, LinkedIn.com<br />
<br />
Paul flagged the following two passages as Forsooths:<br />
<br />
<blockquote><br />
'''The ability to hire well is random.''' “Years ago, we did a study to determine whether anyone at Google is particularly good at hiring,” Bock [Google’s senior vice president for people operations] said. “We looked at tens of thousands of interviews, and everyone who had done the interviews and what they scored the candidate, and how that person ultimately performed in their job. We found zero relationship. It’s a complete random mess, except for one guy who was highly predictive because he only interviewed people for a very specialized area, where he happened to be the world’s leading expert.”<br />
<br><br><br />
'''GPAs don’t predict anything about who is going to be a successful employee.''' “One of the things we’ve seen from all our data crunching is that G.P.A.’s are worthless as a criteria for hiring, and test scores are worthless — no correlation at all except for brand-new college grads, where there’s a slight correlation,” Bock said. “Google famously used to ask everyone for a transcript and G.P.A.’s and test scores, but we don’t anymore, unless you’re just a few years out of school. We found that they don’t predict anything."<br />
</blockquote><br />
<br />
==Dialect maps==<br />
[http://www.huffingtonpost.com/2013/06/06/dialect-maps_n_3395819.html These dialect maps showing the variety of American English have set the internet on fire]<br><br />
by Alexis Kleinman , ''Huffington Post'', 6 June 2013<br />
<br />
A series of data maps depicting word usage and pronunciation differences across America has gone viral on the internet. The maps were produced by North Carolina State University graduate student Joshua Katz, based on [http://www.tekstlab.uio.no/cambridge_survey/ survey research] initiated by Cambridge University linguist Bert Vaux and his colleagues.<br />
<br />
A great deal of information about the project and the mathematics involved can be found at<br />
[http://www4.ncsu.edu/~jakatz2/files/dialectposter.png Katz's poster for his project]. The poster is entitled Beyond "Soda, Pop, or Coke". People with roots in the Boston area may be surprised that "Tonic" didn't make the list! The study also has the perennial favorite of us Easterners who have married someone from elsewhere:<br />
<br />
<center><br />
<br />
'''How do you pronounce Mary/merry/marry?'''<br />
<br />
[[File:MaryMarryMerry.png]]<br />
<br />
</center><br />
<br />
In all, there are 122 sets of maps relating to regional differences in pronunciation and usage. Links are available [http://www4.uwm.edu/FLL/linguistics/dialect/maps.html here] (the above maps are reproduced from Item 15).<br />
<br />
Submitted by Paul Alper<br />
<br />
==Are you scientifically literate?==<br />
<br />
Now you can find out. Physicist and author James Trefil of George Mason University devised a [http://www.thestar.com/life/2009/08/11/scientific_literacy_quiz.html short quiz], which was published in the ''Toronto Star''. There are 26 multiple-choice questions on biology, physics, and chemistry. Score 80% and you make the grade, according to Professor Trefil.<br />
<br />
(I admit to apprehension about taking the test. But I can state, happily, my score met the standard for literacy – a reassuring result for a career scientist.)<br />
<br />
Of course, multiple choice exams have their drawbacks. With four choices per question and no penalty for an incorrect answer, a know-nothing could expect a mark of 25% – still mired in ‘F’, but better than a goose egg.<br />
<br />
For the indecisive and the unlearned, however, there are [http://www.uleth.ca/edu/runte/tests/take/mc/how.html#Tricks more strategic methods] than sheer guessing in multiple choice tests. One ploy, when in doubt, is to choose answer (c). Examiners seem to find this letter a favourite, presumably to conceal the correct answer amongst the wrong ones. Another trick is to pick the longest answer, given that teachers tend to add details to make the correct answer entirely true.<br />
<br />
Adhering to the “choose (c)” rule, I scored 42% on the quiz – significantly better than random (binomial test, P = 0.040, 1-tailed). Choosing the longest answer gave an even more impressive grade of 58% – clearly superior to random picks (P = 0.00039), but still short of true literacy. Alas, it appears there is no substitute to learning science to become scientifically literate.<br />
<br />
Submitted by James Schaefer<br />
<br />
==Twitter settles the argument about geeks versus nerds==<br />
<br />
[http://slackprop.wordpress.com/2013/06/03/on-geek-versus-nerd/ On "Geek" Versus "Nerd"] Burr Settles, June 2, 2013.<br />
<br />
What's the difference between a geek and a nerd? <br />
<br />
<blockquote>To many people, “geek” and “nerd” are synonyms, but in fact they are a little different. Consider the phrase “sports geek” — an occasional substitute for “jock” and perhaps the arch-rival of a “nerd” in high-school folklore. If “geek” and “nerd” are synonyms, then “sports geek” might be an oxymoron. (Furthermore, “sports nerd” either doesn’t compute or means something else.)</blockquote><br />
<br />
Dr. Settles reviews some perspectives on this, but then decides to examine this empirically.<br />
<br />
<blockquote>To characterize the similarities and differences between “geek” and “nerd,” maybe we can find the other words that tend to keep them company, and see if these linguistic companions support my point of view?</blockquote><br />
<br />
Twitter provides one empirical answer.<br />
<br />
<blockquote>I analyzed two sources of Twitter data, since it’s readily available and pretty geeky/nerdy to boot. This includes a background corpus of 2.6 million tweets via the streaming API from between December 6, 2012, and January 3, 2013. I also sampled tweets via the search API matching the query terms “geek” and “nerd” during the same time period (38.8k and 30.6k total, respectively).</blockquote><br />
<br />
The statistic used is pointwise mutual information. You can find a formula for this in the original article. It is effectively the difference in the logarithms between the conditional probability and the unconditional probability.<br />
<br />
<blockquote>The PMI statistic measures a kind of correlation: a positive PMI score for two words means they ”keep great company,” a negative score means they tend to keep their distance, and a score close to zero means they bump into each other more or less at random.</blockquote><br />
<br />
You can graph the PMI for a particular word given "nerd" on one axis of a graph and the PMI for a particular word given "Geek" on the other axis. That produces the following picture, which has been reproduced many times on the Internet.<br />
<br />
[[File:geeknerd-plot-01.png]]<br />
<br />
Words in the lower right corner of the graph (coded in blue) are those that are more strongly associated with "nerd" than "geek". Word in the upper left corner (coded in orange) are more strongly associated with "geek" than "nerd".<br />
<br />
Dr.Settles draws the following conclusion:<br />
<br />
<blockquote>In broad strokes, it seems to me that geeky words are more about stuff (e.g., “#stuff”), while nerdy words are more about ideas (e.g., “hypothesis”). Geeks are fans, and fans collect stuff; nerds are practitioners, and practitioners play with ideas. Of course, geeks can collect ideas and nerds play with stuff, too. Plus, they aren’t two distinct personalities as much as different aspects of personality.</blockquote><br />
<br />
===Questions===<br />
<br />
1. What other statistic might be used instead of PMI to describe the association between various words and "geek/nerd".<br />
<br />
2. The article mentions the Googe ngrams corpus as an alternative source of information. What are the advantages and disadvantages of this source compared to Twitter? Are there other sources of data that could help resolve the geek/nerd distinction?<br />
<br />
3. Is mining data from Twitter an activity more associated with a "geek" or with a "nerd".<br />
<br />
Submitted by Steve Simon</div>Simon66217https://www.causeweb.org/wiki/chance/index.php?title=Chance_News_94&diff=17720Chance News 942013-07-05T20:01:18Z<p>Simon66217: /* Twitter settles the argument about geeks versus nerds */</p>
<hr />
<div>==Quotations==<br />
"...classic PPT statistical graphic: 13 logos, 10 numbers, 9 bubbles, 1 giant green arrow."<br />
<br />
<div align=right> -- Edward Tufte, [https://twitter.com/EdwardTufte/status/342819681054375936/photo/1 tweeting] about the NSA's presentation on its controversial data-collection activities.</div><br />
<br />
[quoted in: [http://www.washingtonpost.com/blogs/wonkblog/wp/2013/06/07/the-real-nsa-scandal-the-horrible-slides/ The real NSA scandal? The horrible slides]. ''Washington Post'', Wonkblog, 7 June 2013.]<br />
<br />
Submitted by Bill Peterson<br />
----<br />
“6. (Mar's Law) Everything is linear if plotted log-log with a fat magic marker.”<br />
<div align=right>from: [http://spacecraft.ssl.umd.edu/old_site/academics/akins_laws.html Akin's Laws of Spacecraft Design]</div><br />
<br />
Submitted by Margaret Cibes<br />
<br />
----<br />
According to [http://en.wikipedia.org/wiki/Derek_J._de_Solla_Price Derek de Solla Price], "...in order to relieve the tedium of sitting for a portrait painter, on two different occasions he [Francis Galton] computed the number of brush strokes and found about 20,000 to the portrait; just the same number, he calculated, as the hand movements that went into the knitting of a pair of socks."<br />
<br />
<div align=right> in: [http://www.kauffman.org/research-and-policy/the-half-life-of-facts.aspx ''The Half-Life of Facts''] by Samuel Arbesman, p. 167</div><br />
<br />
Submitted by Paul Alper<br />
<br />
----<br />
"Statistics is the science that lets you do twenty experiments a year and publish one false result in ''Nature''."<br />
<br />
<div align=right> -- John Maynard Smith ([http://en.wikipedia.org/wiki/John_Maynard_Smith British evolutionary biologist]), quoted in: ''The Half-Life of Facts'', p. 154</div><br />
<br />
Submitted by Paul Alper<br />
<br />
==Forsooth==<br />
“The afternoon wave of [U.S. Open] starters began their first round Thursday in hot, sticky conditions and finished in cool, breezy weather on Friday. Luke Donald described the difference as ‘180 degrees’ ….”<br />
<div align=right>[http://sports.blogs.nytimes.com/2013/06/14/live-updates-follow-fridays-action-at-the-u-s-open/?hp “Mickelson Tied With Horschel for U.S. Open Lead”]<br><br />
by Karen Crouse, <i>The New York Times</i>, June 14, 2013</div><br />
Submitted by Margaret Cibes at the suggestion of Jim Greenwood<br><br />
<br />
----<br />
"Doctors were on board and volunteered to help in 48 percent of cases; nurses and other health workers were available in another 28 percent. Only one-third of cases had to be handled by flight attendants alone."<br />
<div align=right>in: [http://www.heraldnet.com/article/20130530/NEWS02/705309903 50-50 chance of a doctor on board a flight.] ''Herald.Net'', 30 May 2013</div><br />
<br />
Submitted by Bill Peterson<br />
<br />
----<br />
"So for a company that thought there was a 60 percent chance that it would have to pay $1,000 on a claim, and a 40 percent chance it would have to pay $2,000, its required reserve would rise from $1,000, the most probable number, to $1,400 — the average of the probabilities."<br />
<div align=right>in: [http://www.nytimes.com/2013/06/27/business/new-rules-expected-for-insurance-accounting-may-lead-to-erratic-earnings.html New rules expected for insurance accounting may lead to erratic earnings], ''New York Times'', 27 June 2013</div><br />
<br />
Submitted by Bill Peterson<br />
<br />
==Statistics Without Borders==<br />
[http://community.amstat.org/StatisticsWithoutBorders/Home/ Statistics Without Borders]<br><br />
(not to be confused with “<u>Statisticians WithOut</u> Borders), a consulting group)<br><br />
<br />
Current or future statisticians may be interested in the all-volunteer organization Statistics Without Borders. SWB is an Outreach Group of the ASA consisting of over 400 volunteer statisticians who provide free statistical consulting to organizations and government agencies, particularly from developing nations. Its goal is to “promote the use of statistics to improve the health and well-being of all people.”<br> <br />
<br />
The April 2013 issue of <i>Significance</i> magazine contains an article, “Haiti after the earthquake,”[http://www.significancemagazine.org/details/magazine/4705871/Haiti-after-the-earthquake-Statistics-Without-Borders.html] that describes one of their 2010 projects.<br><br />
<br />
Submitted by Margaret Cibes<br />
<br />
==Data about cell and landline phone usage ==<br />
[http://www.nationaljournal.com/blogs/hotlineoncall/2013/06/researchers-warn-of-bias-in-landline-only-phone-polls-18 “Researchers Warn of ‘Bias’ in Landline-Only Phone Polls”]<br><br />
by Steven Shepard, <i>National Journal</i>, June 18, 2013<br><br />
<br />
The CDC reports that landline phone surveys in 2012 were most likely to reach older, whiter Americans. The article gives a number of statistics about landline vs. cell phone usage among various demographic groups. <br />
<br />
(The article also states that it is illegal for automatic dialers to call cell phones, which makes polling cell phone holders more expensive. I’m not sure how many companies observe this ban!)<br />
<br />
One interesting fact, if true: <br />
<blockquote>Calling the proper number of cell phones is not a guarantee of accuracy: Gallup, which called the most cell phones, was considered among the least accurate survey firms in its 2012 pre-election polls; PPP, which called none, was considered among the most accurate.</blockquote><br />
Submitted by Margaret Cibes<br />
<br />
==Google's hiring methodology==<br />
Paul Alper sent a link to the following:<br />
<br />
[http://www.linkedin.com/today/post/article/20130620142512-35894743-on-gpas-and-brain-teasers-new-insights-from-google-on-recruiting-and-hiring On GPAs and brainteasers: New insights from Google on recruiting and hiring], by Adam Bryant, LinkedIn.com<br />
<br />
Paul flagged the following two passages as Forsooths:<br />
<br />
<blockquote><br />
'''The ability to hire well is random.''' “Years ago, we did a study to determine whether anyone at Google is particularly good at hiring,” Bock [Google’s senior vice president for people operations] said. “We looked at tens of thousands of interviews, and everyone who had done the interviews and what they scored the candidate, and how that person ultimately performed in their job. We found zero relationship. It’s a complete random mess, except for one guy who was highly predictive because he only interviewed people for a very specialized area, where he happened to be the world’s leading expert.”<br />
<br><br><br />
'''GPAs don’t predict anything about who is going to be a successful employee.''' “One of the things we’ve seen from all our data crunching is that G.P.A.’s are worthless as a criteria for hiring, and test scores are worthless — no correlation at all except for brand-new college grads, where there’s a slight correlation,” Bock said. “Google famously used to ask everyone for a transcript and G.P.A.’s and test scores, but we don’t anymore, unless you’re just a few years out of school. We found that they don’t predict anything."<br />
</blockquote><br />
<br />
==Dialect maps==<br />
[http://www.huffingtonpost.com/2013/06/06/dialect-maps_n_3395819.html These dialect maps showing the variety of American English have set the internet on fire]<br><br />
by Alexis Kleinman , ''Huffington Post'', 6 June 2013<br />
<br />
A series of data maps depicting word usage and pronunciation differences across America has gone viral on the internet. The maps were produced by North Carolina State University graduate student Joshua Katz, based on [http://www.tekstlab.uio.no/cambridge_survey/ survey research] initiated by Cambridge University linguist Bert Vaux and his colleagues.<br />
<br />
A great deal of information about the project and the mathematics involved can be found at<br />
[http://www4.ncsu.edu/~jakatz2/files/dialectposter.png Katz's poster for his project]. The poster is entitled Beyond "Soda, Pop, or Coke". People with roots in the Boston area may be surprised that "Tonic" didn't make the list! The study also has the perennial favorite of us Easterners who have married someone from elsewhere:<br />
<br />
<center><br />
<br />
'''How do you pronounce Mary/merry/marry?'''<br />
<br />
[[File:MaryMarryMerry.png]]<br />
<br />
</center><br />
<br />
In all, there are 122 sets of maps relating to regional differences in pronunciation and usage. Links are available [http://www4.uwm.edu/FLL/linguistics/dialect/maps.html here] (the above maps are reproduced from Item 15).<br />
<br />
Submitted by Paul Alper<br />
<br />
==Are you scientifically literate?==<br />
<br />
Now you can find out. Physicist and author James Trefil of George Mason University devised a [http://www.thestar.com/life/2009/08/11/scientific_literacy_quiz.html short quiz], which was published in the ''Toronto Star''. There are 26 multiple-choice questions on biology, physics, and chemistry. Score 80% and you make the grade, according to Professor Trefil.<br />
<br />
(I admit to apprehension about taking the test. But I can state, happily, my score met the standard for literacy – a reassuring result for a career scientist.)<br />
<br />
Of course, multiple choice exams have their drawbacks. With four choices per question and no penalty for an incorrect answer, a know-nothing could expect a mark of 25% – still mired in ‘F’, but better than a goose egg.<br />
<br />
For the indecisive and the unlearned, however, there are [http://www.uleth.ca/edu/runte/tests/take/mc/how.html#Tricks more strategic methods] than sheer guessing in multiple choice tests. One ploy, when in doubt, is to choose answer (c). Examiners seem to find this letter a favourite, presumably to conceal the correct answer amongst the wrong ones. Another trick is to pick the longest answer, given that teachers tend to add details to make the correct answer entirely true.<br />
<br />
Adhering to the “choose (c)” rule, I scored 42% on the quiz – significantly better than random (binomial test, P = 0.040, 1-tailed). Choosing the longest answer gave an even more impressive grade of 58% – clearly superior to random picks (P = 0.00039), but still short of true literacy. Alas, it appears there is no substitute to learning science to become scientifically literate.<br />
<br />
Submitted by James Schaefer<br />
<br />
==Twitter settles the argument about geeks versus nerds==<br />
<br />
[http://slackprop.wordpress.com/2013/06/03/on-geek-versus-nerd/ On "Geek" Versus "Nerd"] Burr Settles, June 2, 2013.<br />
<br />
What's the difference between a geek and a nerd? <br />
<br />
<blockquote>To many people, “geek” and “nerd” are synonyms, but in fact they are a little different. Consider the phrase “sports geek” — an occasional substitute for “jock” and perhaps the arch-rival of a “nerd” in high-school folklore. If “geek” and “nerd” are synonyms, then “sports geek” might be an oxymoron. (Furthermore, “sports nerd” either doesn’t compute or means something else.)</blockquote><br />
<br />
Dr. Settles reviews some perspectives on this, but then decides to examine this empirically.<br />
<br />
<blockquote>To characterize the similarities and differences between “geek” and “nerd,” maybe we can find the other words that tend to keep them company, and see if these linguistic companions support my point of view?</blockquote><br />
<br />
Twitter provides one empirical answer.<br />
<br />
<blockquote>I analyzed two sources of Twitter data, since it’s readily available and pretty geeky/nerdy to boot. This includes a background corpus of 2.6 million tweets via the streaming API from between December 6, 2012, and January 3, 2013. I also sampled tweets via the search API matching the query terms “geek” and “nerd” during the same time period (38.8k and 30.6k total, respectively).</blockquote><br />
<br />
The statistic used is pointwise mutual information. You can find a formula for this in the original article. It is effectively the difference in the logarithms between the conditional probability and the unconditional probability.<br />
<br />
<blockquote>The PMI statistic measures a kind of correlation: a positive PMI score for two words means they ”keep great company,” a negative score means they tend to keep their distance, and a score close to zero means they bump into each other more or less at random.</blockquote><br />
<br />
You can graph the PMI for a particular word given "nerd" on one axis of a graph and the PMI for a particular word given "Geek" on the other axis. That produces the following picture, which has been reproduced many times on the Internet.<br />
<br />
[[File:geeknerd-plot-01.png]]<br />
<br />
Words in the lower right corner of the graph (coded in blue) are those that are more strongly associated with "nerd" than "geek". Word in the upper left corner (coded in orange) are more strongly associated with "geek" than "nerd".<br />
<br />
<br />
<br />
Submitted by Steve Simon</div>Simon66217https://www.causeweb.org/wiki/chance/index.php?title=File:Geeknerd-plot-01.png&diff=17719File:Geeknerd-plot-01.png2013-07-05T19:57:44Z<p>Simon66217: </p>
<hr />
<div></div>Simon66217https://www.causeweb.org/wiki/chance/index.php?title=Chance_News_94&diff=17715Chance News 942013-07-05T19:46:30Z<p>Simon66217: /* Are you scientifically literate? */</p>
<hr />
<div>==Quotations==<br />
"...classic PPT statistical graphic: 13 logos, 10 numbers, 9 bubbles, 1 giant green arrow."<br />
<br />
<div align=right> -- Edward Tufte, [https://twitter.com/EdwardTufte/status/342819681054375936/photo/1 tweeting] about the NSA's presentation on its controversial data-collection activities.</div><br />
<br />
[quoted in: [http://www.washingtonpost.com/blogs/wonkblog/wp/2013/06/07/the-real-nsa-scandal-the-horrible-slides/ The real NSA scandal? The horrible slides]. ''Washington Post'', Wonkblog, 7 June 2013.]<br />
<br />
Submitted by Bill Peterson<br />
----<br />
“6. (Mar's Law) Everything is linear if plotted log-log with a fat magic marker.”<br />
<div align=right>from: [http://spacecraft.ssl.umd.edu/old_site/academics/akins_laws.html Akin's Laws of Spacecraft Design]</div><br />
<br />
Submitted by Margaret Cibes<br />
<br />
----<br />
According to [http://en.wikipedia.org/wiki/Derek_J._de_Solla_Price Derek de Solla Price], "...in order to relieve the tedium of sitting for a portrait painter, on two different occasions he [Francis Galton] computed the number of brush strokes and found about 20,000 to the portrait; just the same number, he calculated, as the hand movements that went into the knitting of a pair of socks."<br />
<br />
<div align=right> in: [http://www.kauffman.org/research-and-policy/the-half-life-of-facts.aspx ''The Half-Life of Facts''] by Samuel Arbesman, p. 167</div><br />
<br />
Submitted by Paul Alper<br />
<br />
----<br />
"Statistics is the science that lets you do twenty experiments a year and publish one false result in ''Nature''."<br />
<br />
<div align=right> -- John Maynard Smith ([http://en.wikipedia.org/wiki/John_Maynard_Smith British evolutionary biologist]), quoted in: ''The Half-Life of Facts'', p. 154</div><br />
<br />
Submitted by Paul Alper<br />
<br />
==Forsooth==<br />
“The afternoon wave of [U.S. Open] starters began their first round Thursday in hot, sticky conditions and finished in cool, breezy weather on Friday. Luke Donald described the difference as ‘180 degrees’ ….”<br />
<div align=right>[http://sports.blogs.nytimes.com/2013/06/14/live-updates-follow-fridays-action-at-the-u-s-open/?hp “Mickelson Tied With Horschel for U.S. Open Lead”]<br><br />
by Karen Crouse, <i>The New York Times</i>, June 14, 2013</div><br />
Submitted by Margaret Cibes at the suggestion of Jim Greenwood<br><br />
<br />
----<br />
"Doctors were on board and volunteered to help in 48 percent of cases; nurses and other health workers were available in another 28 percent. Only one-third of cases had to be handled by flight attendants alone."<br />
<div align=right>in: [http://www.heraldnet.com/article/20130530/NEWS02/705309903 50-50 chance of a doctor on board a flight.] ''Herald.Net'', 30 May 2013</div><br />
<br />
Submitted by Bill Peterson<br />
<br />
----<br />
"So for a company that thought there was a 60 percent chance that it would have to pay $1,000 on a claim, and a 40 percent chance it would have to pay $2,000, its required reserve would rise from $1,000, the most probable number, to $1,400 — the average of the probabilities."<br />
<div align=right>in: [http://www.nytimes.com/2013/06/27/business/new-rules-expected-for-insurance-accounting-may-lead-to-erratic-earnings.html New rules expected for insurance accounting may lead to erratic earnings], ''New York Times'', 27 June 2013</div><br />
<br />
Submitted by Bill Peterson<br />
<br />
==Statistics Without Borders==<br />
[http://community.amstat.org/StatisticsWithoutBorders/Home/ Statistics Without Borders]<br><br />
(not to be confused with “<u>Statisticians WithOut</u> Borders), a consulting group)<br><br />
<br />
Current or future statisticians may be interested in the all-volunteer organization Statistics Without Borders. SWB is an Outreach Group of the ASA consisting of over 400 volunteer statisticians who provide free statistical consulting to organizations and government agencies, particularly from developing nations. Its goal is to “promote the use of statistics to improve the health and well-being of all people.”<br> <br />
<br />
The April 2013 issue of <i>Significance</i> magazine contains an article, “Haiti after the earthquake,”[http://www.significancemagazine.org/details/magazine/4705871/Haiti-after-the-earthquake-Statistics-Without-Borders.html] that describes one of their 2010 projects.<br><br />
<br />
Submitted by Margaret Cibes<br />
<br />
==Data about cell and landline phone usage ==<br />
[http://www.nationaljournal.com/blogs/hotlineoncall/2013/06/researchers-warn-of-bias-in-landline-only-phone-polls-18 “Researchers Warn of ‘Bias’ in Landline-Only Phone Polls”]<br><br />
by Steven Shepard, <i>National Journal</i>, June 18, 2013<br><br />
<br />
The CDC reports that landline phone surveys in 2012 were most likely to reach older, whiter Americans. The article gives a number of statistics about landline vs. cell phone usage among various demographic groups. <br />
<br />
(The article also states that it is illegal for automatic dialers to call cell phones, which makes polling cell phone holders more expensive. I’m not sure how many companies observe this ban!)<br />
<br />
One interesting fact, if true: <br />
<blockquote>Calling the proper number of cell phones is not a guarantee of accuracy: Gallup, which called the most cell phones, was considered among the least accurate survey firms in its 2012 pre-election polls; PPP, which called none, was considered among the most accurate.</blockquote><br />
Submitted by Margaret Cibes<br />
<br />
==Google's hiring methodology==<br />
Paul Alper sent a link to the following:<br />
<br />
[http://www.linkedin.com/today/post/article/20130620142512-35894743-on-gpas-and-brain-teasers-new-insights-from-google-on-recruiting-and-hiring On GPAs and brainteasers: New insights from Google on recruiting and hiring], by Adam Bryant, LinkedIn.com<br />
<br />
Paul flagged the following two passages as Forsooths:<br />
<br />
<blockquote><br />
'''The ability to hire well is random.''' “Years ago, we did a study to determine whether anyone at Google is particularly good at hiring,” Bock [Google’s senior vice president for people operations] said. “We looked at tens of thousands of interviews, and everyone who had done the interviews and what they scored the candidate, and how that person ultimately performed in their job. We found zero relationship. It’s a complete random mess, except for one guy who was highly predictive because he only interviewed people for a very specialized area, where he happened to be the world’s leading expert.”<br />
<br><br><br />
'''GPAs don’t predict anything about who is going to be a successful employee.''' “One of the things we’ve seen from all our data crunching is that G.P.A.’s are worthless as a criteria for hiring, and test scores are worthless — no correlation at all except for brand-new college grads, where there’s a slight correlation,” Bock said. “Google famously used to ask everyone for a transcript and G.P.A.’s and test scores, but we don’t anymore, unless you’re just a few years out of school. We found that they don’t predict anything."<br />
</blockquote><br />
<br />
==Dialect maps==<br />
[http://www.huffingtonpost.com/2013/06/06/dialect-maps_n_3395819.html These dialect maps showing the variety of American English have set the internet on fire]<br><br />
by Alexis Kleinman , ''Huffington Post'', 6 June 2013<br />
<br />
A series of data maps depicting word usage and pronunciation differences across America has gone viral on the internet. The maps were produced by North Carolina State University graduate student Joshua Katz, based on [http://www.tekstlab.uio.no/cambridge_survey/ survey research] initiated by Cambridge University linguist Bert Vaux and his colleagues.<br />
<br />
A great deal of information about the project and the mathematics involved can be found at<br />
[http://www4.ncsu.edu/~jakatz2/files/dialectposter.png Katz's poster for his project]. The poster is entitled Beyond "Soda, Pop, or Coke". People with roots in the Boston area may be surprised that "Tonic" didn't make the list! The study also has the perennial favorite of us Easterners who have married someone from elsewhere:<br />
<br />
<center><br />
<br />
'''How do you pronounce Mary/merry/marry?'''<br />
<br />
[[File:MaryMarryMerry.png]]<br />
<br />
</center><br />
<br />
In all, there are 122 sets of maps relating to regional differences in pronunciation and usage. Links are available [http://www4.uwm.edu/FLL/linguistics/dialect/maps.html here] (the above maps are reproduced from Item 15).<br />
<br />
Submitted by Paul Alper<br />
<br />
==Are you scientifically literate?==<br />
<br />
Now you can find out. Physicist and author James Trefil of George Mason University devised a [http://www.thestar.com/life/2009/08/11/scientific_literacy_quiz.html short quiz], which was published in the ''Toronto Star''. There are 26 multiple-choice questions on biology, physics, and chemistry. Score 80% and you make the grade, according to Professor Trefil.<br />
<br />
(I admit to apprehension about taking the test. But I can state, happily, my score met the standard for literacy – a reassuring result for a career scientist.)<br />
<br />
Of course, multiple choice exams have their drawbacks. With four choices per question and no penalty for an incorrect answer, a know-nothing could expect a mark of 25% – still mired in ‘F’, but better than a goose egg.<br />
<br />
For the indecisive and the unlearned, however, there are [http://www.uleth.ca/edu/runte/tests/take/mc/how.html#Tricks more strategic methods] than sheer guessing in multiple choice tests. One ploy, when in doubt, is to choose answer (c). Examiners seem to find this letter a favourite, presumably to conceal the correct answer amongst the wrong ones. Another trick is to pick the longest answer, given that teachers tend to add details to make the correct answer entirely true.<br />
<br />
Adhering to the “choose (c)” rule, I scored 42% on the quiz – significantly better than random (binomial test, P = 0.040, 1-tailed). Choosing the longest answer gave an even more impressive grade of 58% – clearly superior to random picks (P = 0.00039), but still short of true literacy. Alas, it appears there is no substitute to learning science to become scientifically literate.<br />
<br />
Submitted by James Schaefer<br />
<br />
==Twitter settles the argument about geeks versus nerds==<br />
<br />
[http://slackprop.wordpress.com/2013/06/03/on-geek-versus-nerd/ On "Geek" Versus "Nerd"] Burr Settles, June 2, 2013.<br />
<br />
What's the difference between a geek and a nerd? <br />
<br />
<blockquote>To many people, “geek” and “nerd” are synonyms, but in fact they are a little different. Consider the phrase “sports geek” — an occasional substitute for “jock” and perhaps the arch-rival of a “nerd” in high-school folklore. If “geek” and “nerd” are synonyms, then “sports geek” might be an oxymoron. (Furthermore, “sports nerd” either doesn’t compute or means something else.)</blockquote><br />
<br />
Dr. Settles reviews some perspectives on this, but then decides to examine this empirically.<br />
<br />
<blockquote>To characterize the similarities and differences between “geek” and “nerd,” maybe we can find the other words that tend to keep them company, and see if these linguistic companions support my point of view?</blockquote><br />
<br />
Twitter provides one empirical answer.<br />
<br />
<blockquote>I analyzed two sources of Twitter data, since it’s readily available and pretty geeky/nerdy to boot. This includes a background corpus of 2.6 million tweets via the streaming API from between December 6, 2012, and January 3, 2013. I also sampled tweets via the search API matching the query terms “geek” and “nerd” during the same time period (38.8k and 30.6k total, respectively).</blockquote><br />
<br />
The statistic used is pointwise mutual information.<br />
<br />
Submitted by Steve Simon</div>Simon66217https://www.causeweb.org/wiki/chance/index.php?title=Chance_News_92&diff=17210Chance News 922013-03-27T19:56:41Z<p>Simon66217: /* Discussion */</p>
<hr />
<div>==Quotations==<br />
"I've done the calculation and your chances of winning the lottery are identical whether you play or not."<br />
<div align=right>--Fran Lebowitz (American author and humorist)</div><br />
<br />
Suggested by Naomi Neff (with thanks to Cynthia Slater)<br />
<br />
----<br />
"As much as it pleases me to see statistical data introduced in the Supreme Court, the act of citing statistical factoids is not the same thing as drawing sound inferences from them."<br />
<br />
<div align=right>--Nate Silver, [http://fivethirtyeight.blogs.nytimes.com/2013/03/07/in-supreme-court-debate-on-voting-rights-act-a-dubious-use-of-statistics/ In Supreme Court Debate on Voting Rights Act, a Dubious Use of Statistics] FiveThirtyEight blog</div><br />
<br />
Submitted by Paul Alper<br />
<br />
----<br />
“economisting … 1. The act or process of converting limited evidence into grand claims by means of punning, multiplicity of meaning, and over-reaching. 2. The belief or practice that empirical evidence can only confirm and never disconfirm a favored theory. 3. Conclusions that are theory-driven, not evidence-based.”<br />
<div align=right>Anthropologist Clifford Geertz, <i>Available Light: Anthropological Reflections on Philosophical Topics</i>, Princeton, 2000<br><br />
quoted by Edward Tufte in his [http://www.edwardtufte.com/bboard/q-and-a-fetch-msg?msg_id=0001Zl <i>Beautiful Evidence</i>], Graphics Press, 2006</div><br />
Submitted by Margaret Cibes<br />
<br />
----<br />
"Statistics books almost always illustrate this point by drawing colored marbles out of an urn. (In fact, it's about the only place where one sees the word 'urn' used with any regularity.)<br />
<br />
<div align=right>--Charles Wheelan, ''Naked Statistics'' (p. 112)</div><br />
<br />
Submitted by Paul Alper<br />
<br />
==Forsooth==<br />
Miracles of the loaves and fishes, from <i>The Wall Street Journal</i> ....<br><br />
<br />
“The quants have arrived at the Academy [of Motion Picture Arts and Sciences]. …. The goals in making ... predictions extend beyond [Oscar night]. Dr. Rothschild [Microsoft Research economist] is testing whether surveying people online about Oscar patterns—for example, does winning best-adapted screenplay correspond with winning best picture?—is a method that can be translated to forecasting in other areas. If it works, ‘We can apply it to all sorts of other things we don't have data for,’ Dr. Rothschild said."<br />
<div align=right>Carl Bialik in [http://online.wsj.com/article/SB10001424127887324503204578318682787064790.html?KEYWORDS=carl+bialik#articleTabs%3Darticle “And the Oscar-Pool Winners Are...the Stats Dudes”]<br><br />
by Carl Bialik, February 23, 2013</div><br />
<br />
<center>[[file:Extrapolation.jpg|150px]]</center><br />
<div align=right>[http://online.wsj.com/article/SB10001424127887324077704578358231107272180.html?mg=id-wsj “Big Data Broadens Its Range”], March 13, 2013</div><br />
Submitted by Margaret Cibes<br />
<br />
==Simpson’s paradox and the ecological fallacy==<br />
<br />
The lay public tends to believe that statistics is merely a (rather dull) branch of mathematics. In fact, the discipline of statistics should be viewed as a science, as exemplified by physics, astronomy, chemistry, etc., which uses mathematics extensively and is situation dependent. In other word, the same numbers lead to different conclusion depending on the context.<br />
<br />
Prime examples of situation dependency may be found in the discussions of Simpson’s paradox and the even more subtle phenomenon known as the ecological fallacy. A treatment of the former can sometimes be found in elementary statistics textbooks but the latter, being less intuitive, is relatively rare in textbooks but often pops up in learned discussions where the reader is warned about drawing false conclusions.<br />
<br />
The dating of the phenomenon now known as Simpson’s paradox goes back before any of the current Chance News readers were born; the bestowing of the name, according to [http://en.wikipedia.org/wiki/Simpson's_paradox Wikipedia], originated much later in 1971:<br />
<blockquote><br />
Simpson's paradox (or the Yule–Simpson effect) is a paradox in which a trend [i.e., inequality] that appears in different groups of data disappears when these groups are combined, and the reverse trend [i.e., opposite inequality] appears for the aggregate data. This result is often encountered in social-science and medical-science statistics, and is particularly confounding when frequency data are unduly given causal interpretations.<br />
</blockquote><br />
The Wikipedia article has this “real-life example from a medical study comparing the success rates of two treatments for kidney stones.”<br />
<br />
<table class="wikitable" summary="results accounting for stone size" style="margin-left:auto; margin-right:auto;"><br />
<tr><br />
<th></th><br />
<th>Treatment A</th><br />
<th>Treatment B</th><br />
</tr><br />
<tr align="center"><br />
<th>Small Stones</th><br />
<td><i>Group 1</i><br /><br />
<b>93% (81/87)</b></td><br />
<td><i>Group 2</i><br /><br />
87% (234/270)</td><br />
</tr><br />
<tr align="center"><br />
<th>Large Stones</th><br />
<td><i>Group 3</i><br /><br />
<b>73% (192/263)</b></td><br />
<td><i>Group 4</i><br /><br />
69% (55/80)</td><br />
</tr><br />
<tr align="center"><br />
<th>Both</th><br />
<td>78% (273/350)</td><br />
<td><b>83% (289/350)</b></td><br />
</tr><br />
</table><br />
<blockquote><br />
The paradoxical conclusion is that treatment A is more effective when used on small stones, [93% > 87%] and also when used on large stones, [73% > 69%] yet treatment B is more effective when considering both sizes at the same time [78% < 83%]. In this example, the "lurking" variable (or confounding variable) of the stone size was not previously known to be important until its effects were included.<br />
</blockquote><br />
In this context of kidney stones, it is clear that disaggregation makes sense and Treatment A is preferable to Treatment B despite Treatment B being better in the aggregate sense. However, if we take the same numbers but change the context to Athletic Team A and Athletic Team B who play Small and Large opponents and the only thing that determines ranking is the total winning percentage, then Athletic Team B is preferred to Athletic Team A. That is, aggregation makes sense in this scenario as it did not in the original Wikipedia presentation. <br />
<br />
Other interesting examples are provided in the Wikipedia article. When money is at stake, as in the “Berkeley gender bias” case discussed in Wikipedia, finding a lurking (confounding) variable requires some clever slicing to find “Small” and “Large” which will reverse the inequality. The Wikipedia article also refers to the so-called “low birth rate paradox” whereby “it has been observed that babies of low birth weights born to smoking mothers have a lower mortality rate than the babies of low birth weights of non-smokers.” The paradoxical implication is that smoking helps to lower mortality of newborns. More on this below.<br />
<br />
[http://en.wikipedia.org/wiki/Ecological_fallacy A different Wikipedia article] has two definitions of the ecological fallacy. The first definition focuses on aggregation and disaggregation. With this definition, Simpson’s paradox is subsumed under the ecological fallacy:<br />
<blockquote><br />
An ecological fallacy (or ecological inference fallacy) is a logical fallacy in the interpretation of statistical data where inferences about the nature of individuals [disaggregation] are deduced from inference for the group [aggregation] to which those individuals belong. <br />
</blockquote><br />
The second definition spotlights the notion of correlation:<br />
<blockquote><br />
<br />
Ecological fallacy can refer to the following statistical fallacy: the correlation between individual variables is deduced from the correlation of the variables collected for the group to which those individuals belong.<br />
</blockquote><br />
<br />
Although elementary statistics textbooks do not customarily mention the ecological fallacy, it is even older than Simpson’s paradox. The term was first coined in 1950 by William Robinson but goes back to Emile Durkheim’s 1897 study of suicide. [http://www.teachepi.org/documents/courses/bfiles/The%20B%20Files_File3_Durkheim_Final_Complete.pdf From the graph], it appears that the greater the proportion of Protestants, the greater is the suicide rate:<br />
<br />
<center>[[File:Suicide.png | 600px]]</center><br />
<br />
<blockquote><br />
According to Morgenstern, the estimated rate ratio of 7.6 was probably not because suicide rates were nearly 8 fold higher in Protestants than in non-Protestants. Rather, because none of the regions was entirely Protestant or non-Protestant, it may have been non-Protestants (primarily Catholics) who were committing suicide in predominantly Protestant provinces. It is plausible that members of a religious minority might have been more likely to commit suicide than were members of the majority. Living in a <br />
predominantly Protestant area had a contextual effect on suicide risk among Catholics.<br />
<br><br><br />
Interestingly, Morgenstern points out that Durkheim compared the suicide rates at the individual level for Protestants, Catholics and Jews living in Prussia, and from his data, the rate was about twice as great in Protestants as in other religious groups. Thus, when the rate ratios are compared (2 vs 8), there appears to be substantial ecological bias using the aggregate level data.<br />
</blockquote><br />
<br />
In the above situation there was no reversal of an inequality, merely a sharp diminishing from aggregated to disaggregated. The following example of the ecological fallacy actually illustrates the reversal. <br />
<blockquote><br />
<br />
One compelling example by Robinson (1950), was the relationship between nativity (foreign vs native born) and literacy. For each of the 48 states in the USA of 1930, [there were only 48 states admitted to the Union by 1930] Robinson computed two numbers: the percent of the population who were foreign-born (i.e. immigrants), and the percent who were literate. He found the correlation between the 48 pairs of numbers was .53. This ecological correlation suggested a positive association between foreign birth and literacy: the foreign-born (immigrants) are more likely to be literate than the native-born. In reality, the association was negative: the correlation computed at the individual level was −0.11 (immigrants were less literate than native citizens). The ecological correlation gave the incorrect inference. This is because the foreign-born (immigrants) tended to migrate to and settle in states where the native-born are relatively literate. In this example by Robinson, the correlation is totally reversed. <br />
</blockquote><br />
<br />
[http://ije.oxfordjournals.org/content/40/4/1123.full Robinson’s data] look this way:<br />
<br />
<center> [[File:Robinson.png | 450 px]] </center><br />
<br />
[http://blog.statwing.com/the-ecological-fallacy/ The following graph] dealing this time with income and being foreign born is even more striking:<br />
<br />
<br />
<center> [[File:Income.png | 450 px]] </center><br />
<blockquote><br />
U.S. states with proportionally more immigrants have proportionally more households with income above $100k. Ergo, immigrants are more likely than non-immigrants to have household incomes above $100k.<br />
<br><br><br />
Hopefully something feels off about that logic. Because it’s wrong. Actually the relationship between income and being an immigrant at the individual level is the opposite.<br />
<br><br><br />
<center> [[File:Foreign-Born-vs-Income-Indiv.png | 250 px]] </center><br />
Deducing from the first chart that immigrants are more likely to be well-off is committing the ecological fallacy—attributing qualities at the individual level because of a relationship at a group level.<br />
</blockquote><br />
But here is a more recent and more difficult-to-unravel ecological fallacy:<br />
<br />
<blockquote><br />
That example was pretty easy to catch, not least because it feels intuitive that immigrants would tend to have lower income than non-immigrants. <br />
<br><br><br />
But not all ecological fallacies are so easy to spot.<br />
For example, there’s a negative correlation between per capita income in a state and the percent of the 2012 presidential election vote that went to Romney.<br />
<center> [[File: Income-vs-Republican.png| 450 px]] </center><br />
<br><br><br />
It’s easy to picture rich and liberal cities like San Francisco and New York, hear the phrase “latte liberal” a couple times, and believe that higher income is in fact correlated with voting Democratic.<br />
At an individual level, though, higher income is associated with voting Republican.<br />
<center> [[File: Republican-Vote-Share.jpg | 350 px]] </center><br />
The (simplified) explanation for this apparent paradox? Across the country, lower income folk tend to vote Democrat; within blue states, upper income folk also vote Democrat, but in red states they vote Republican. <br />
</blockquote><br />
<br />
A general way to look at where the fallacy might arise is via the [http://www.teachepi.org/documents/courses/bfiles/The%20B%20Files_File3_Durkheim_Final_Complete.pdf following graph and explanation of Durkheim’s suicide data]:<br />
<br />
<center>[[File:Dirkheim_expl.png | 600px]]</center><br />
<br />
That is, within every group it is possible that even if the correlation (regression line) is negative, it can happen that across the groups, the correlation (regression line) is positive. Note too that in many situations the “within” is not a cloud of points, each of which represents an individual, but instead, there is just one point, average exposure and average outcome. Further, exposure may come from one data base and outcome from another data base. This is totally unlike the kidney stones example which began this wiki because there stone and success can be tied to a particular individual. <br />
<br />
===Discussion===<br />
<br />
1. An oft-used synonym for the ecological fallacy (inferring from group to individuals) is called cross level inference. The opposite of the ecological fallacy is the atomistic fallacy (inferring from the individuals to the group).<br />
<br />
2. With regard to Robinson’s data, besides the fallacy aspect, what is wrong with doing a correlation in the first place?<br />
<br />
3. Concerning the graph of foreign born and income, suppose the ordinates were interchanged. How is this then similar to Durkheim’s study and its ecological fallacy?<br />
<br />
4. [http://en.wikipedia.org/wiki/Low_birth_weight_paradox The paradox of the smoking mother] is supposedly explained by the following:<br />
<blockquote><br />
The birth weight distribution for children of smoking mothers is shifted to lower weights by their mothers' actions. Therefore, otherwise healthy babies (who would weigh more if it were not for the fact their mother smoked) are born underweight. They have a lower mortality rate than children who have other medical reasons why they are born underweight, regardless of the fact their mother does not smoke.<br />
In short, smoking may be harmful in that it contributes to low birth weight, but other causes of low birth weight are generally more harmful only with regard to their weight.<br />
</blockquote><br />
How does this explanation accord with the aforementioned phrase, situation dependent?<br />
<br />
Submitted by Paul Alper<br />
<br />
==Normal vs. paranormal==<br />
John Allen Paulos sent a link to the following cartoon, reproduced below as presented on the StackExchange blog [http://stats.stackexchange.com/posts/14356/revisions Cross Validated]:<br />
<center>[[File:T2XrE.gif]]<br />
<br>From: '''A visual comparison of normal and paranormal distributions'''<br> Matthew Freeman ''J Epidemiol Community Health'' 2006;60:6. <br>Lower caption says 'Paranormal Distribution'- no idea why the graphical artifact is occuring.<br />
</center><br />
<br />
==Gallup reviewing its methods==<br />
[http://www.huffingtonpost.com/2013/03/08/gallup-presidential-poll_n_2806361.html “Gallup Presidential Poll: How Did Brand-Name Firm Blow Election?”]<br><br />
<i>HuffPost Pollster</i>, March 8, 2013<br><br />
<br />
The article discusses Gallup’s consistently favorable-to-Romney poll results over the Fall 2012 presidential election cycle, including a final Romney 49%-Obama 48% result. (Of course, 49 to 48 does not a winning prediction make.) It includes a nice scatterplot illustrating that the Gallup results deviated remarkably (not necessarily “significantly”) from other national polls over this period.<br><br />
<br />
Apparently Gallup revised its methodology re presidential approval polling in October 2012, in order to correct an “under-representation of non-whites in its samples.” Another nice scatterplot shows how Gallup’s rating results moved more into line with other polls’ results in October of the period July 2012-January 2013.<br><br />
<br />
The article also contains a somewhat detailed discussion of two serious problems facing Gallup and other pollsters today: “how they treat their ‘likely voter’ models and how they draw their samples from the general population.” These are issues associated with identifying likely voters and with reaching them by phone.<br><br />
<br />
Submitted by Margaret Cibes<br />
<br />
==Naked Statistics== <br />
<br />
Charles Wheelan’s book, ''Naked Statistics: Stripping the Dread from the Data'', is a breezy fun-filled read, his “homage to an earlier W.W. Norton classic, ''How to Lie with Statistics'' by Daryll Huff. Without my wishing to imply anything negative, a few decades back, ''Naked Statistics'' would be an ideal text for a course entitled, “Statistics for Poets.” Today, even poets, drama students, and people whose specialty is 17th century French drama (perhaps unfortunately) really need to learn some basic statistics. From the very Introduction to the book, he emphasizes his distaste for mathematics for mathematics sake: “What is the area beneath a parabola? Who Cares?” Yet, he likes physics which uses the same math “Because physics has a clear purpose.” Likewise, “I love statistics,” a comment not often seen or heard outside of Chance News. <br />
<br />
As he puts it<br />
<blockquote><br />
The paradox of statistics is that they are everywhere--from batting averages to presidential polls--but the discipline itself has a reputation for being uninteresting and inaccessible. Many statistics books and classes are overly laden with math and jargon. Believe me, the technical details are crucial (and interesting)--but it’s just Greek if you don’t understand the intuition. And you may not even care about the intuition if you’re not convinced that there is any reason to learn it. Every chapter in this book promises to answer the basic question that I asked (to no effect) of my high school calculus teacher: What is the point of this?<br />
<br><br><br />
The point is that statistics helps process data, which is really just a fancy name for information.<br />
</blockquote><br />
<br />
His motto is “Statistics can be really interesting, and most of it isn’t that difficult.” By the end of the book the reader is confronting regression analysis, which he calls “the miracle elixir, and in the next chapter, why it may not be. His examples vary from the amusingly bizarre to the downright practical. ''Naked Statistics'' is an ideal gift to a significant other who loves you but wonders about what you actually do with your time.<br />
<br />
'''Discussion'''<br />
<br />
1. On page xii he reveals “a career epiphany” he had at math camp. The math teacher was describing without any physical context that the infinite (geometric) series <br />
1+1/2 + 1/4 + 1/8 +…converges to a finite number. Wheelan came up with the following context to make it meaningful to him: A wall is two feet away and your first move is one foot, followed by a move of 1/2 foot, followed by a move of 1/4 foot and so on until you are “pretty darn close to the wall.” What would happen to you and the wall if the infinite series was instead 1+1/2 + 1/3 +1/4 + 1/5 +1/6 + 1/7 +1/8 +…?<br />
<br />
2. Nate Silver’s book, ''The Signal and the Noise'', is a hymn to Bayesian statistics. ‘’Naked Statistics’’ has no mention whatever of Bayes or Silver so that your significant other will have to do some outside reading. Wheelan promises that his second edition will include Bayesian concepts.<br />
<br />
3. [http://www.nytimes.com/2013/01/29/science/naked-statistics-by-charles-wheelan-review.html The review in the ''NYT''] put it this way: <br />
<br />
<blockquote><br />
While a great measure of the book’s appeal comes from Mr. Wheelan’s fluent style — a natural comedian, he is truly the Dave Barry of the coin toss set — the rest comes from his multiple real world examples illustrating exactly why even the most reluctant mathophobe is well advised to achieve a personal understanding of the statistical underpinnings of life, whether that individual is watching football on the couch, picking a school for the children or jiggling anxiously in a hospital admitting office.<br><br><br />
Are you a fan of those handy ranking systems based on performance data, guaranteed to steer you to the best surgeons in town? If so, you are up to your armpits in descriptive statistics, and Mr. Wheelan has some advice for you: beware. The easiest way for doctors to game those numbers is by avoiding the sickest patients.<br />
</blockquote><br />
<br />
How do college football and basketball teams similarly game the numbers?<br />
<br />
4. At the same ''NYT'' review there is an accompanying graphic taken from Wheelan’s book:<br />
<center>[[File:NYT_29scibooks-graphic-popup.jpg | 400px]]</center><br />
From the graphic, why would a (Pearson product-moment) correlation be misleading? Why the “reverse causality”?<br />
<br />
[Note: The ''NYT'' also provided [http://graphics8.nytimes.com/packages/pdf/science/naked-stats-excerpt.pdf this excerpt] from the book's introductory chapter.]<br />
<br />
Submitted by Paul Alper<br />
<br />
==Miscellaneous stats news==<br />
From <i>The Wall Street Journal</i>:<br><br />
<br />
"One [issue] is, if we see a sequence of words, how can we best guess which word is likely to come next. …. The other is how does that relate to the way a user actually interacts with their [sic] touch screen. The way we do this is essentially by modeling the surface of the keyboard as a series of probability distributions. What that means in layman’s terms is, the keyboard looks a bit like a mountain range with a peak where the user perceives each of the keys to be. We collect the points that you touch the screen, and we form and mold the mountains around those points. That gives us a unique snapshot of the way you perceive your keyboard. If we solve that problem, that gives us probabilities we can also use with the language probabilities we have, and then we tie these things together. What comes out at the end is the solution to this central mathematical problem — how do I guess what the user is trying to say.”<br />
<div align=right>Tech officer for Android in [http://blogs.wsj.com/digits/2013/03/18/the-science-behind-guessing-what-youll-type-next/?KEYWORDS=from+a+molehill+to+a+mountain+-+graphic “The Science Behind What You’ll Type Next”], March 18, 2013</div><br />
<br />
“The training of data scientists hasn't caught up with that demand, leaving companies searching for talent and especially, some say, for the relatively few people with extensive experience in the field. …. Tech workers with a full complement of big-data analysis skills are paid on average 11.5% more than people without those skills ….”<br />
<div align=right>[http://online.wsj.com/article/SB10001424127887323549204578319930378618430.html?KEYWORDS=spencer+e+ante “Help Wanted! Data, data everywhere – and not enough people to decipher it”], March 8, 2013</div><br />
<br />
“Poring once more over a 12-year-old set of data on breast-cancer tumors, Dr. Lum saw correlations between the disease and patients' outcomes that she and her fellow researchers had never noticed before …. Dr. Lum's new view came courtesy of software that uses topology, a branch of math that compresses relationships in complex data into shapes researchers can manipulate and probe: in this case, a Y, like a two-eared worm. …. [R]esearchers increasingly are scouring scientific papers and esoteric branches of mathematics like topology to make sense of complex data sets. …. Using graph theory, a tool similar to topology, IBM is mapping interactions of people on social networks, including its own.”<br />
<div align=right>[http://online.wsj.com/article/SB10001424127887323452204578288264046780392.html?KEYWORDS=deborah+gage “The New Shape of Big Data”], March 8, 2013</div><br />
<br />
Also so [http://online.wsj.com/article/SB10001424127887324196204578298381588348290.html?KEYWORDS=shira+ovide “Big Data, Big Blunders”], March 8, 2013<br><br />
<br />
Submitted by Margaret Cibes<br />
==Mediterranean diet==<br />
[http://www.nytimes.com/2013/02/26/health/mediterranean-diet-can-cut-heart-disease-study-finds.html?pagewanted=all&_r=1& Mediterranean diet shown to ward off heart attack and stroke]<br><br />
by Gina Kolata, ''New York Times'', 25 February 2013<br />
<br />
Diets come and diets go: high protein, Atkins, South Beach, Dash, Weight Watchers, low carb, no carb. But then there is the perennial favorite, the so-called Mediterranean diet which has generated some recent positive publicity. According to the NYT article:<br />
<blockquote><br />
The findings, published on The New England Journal of Medicine’s Web site on Monday, were based on the first major clinical trial to measure the diet’s effect on heart risks. The magnitude of the diet’s benefits startled experts. The study ended early, after almost five years, because the results were so clear it was considered unethical to continue.<br />
</blockquote><br />
According to someone who was not connected with this study conducted from Spain,<br />
<blockquote><br />
“And the really important thing — the coolest thing — is that they used very meaningful endpoints. They did not look at risk factors like cholesterol or hypertension or weight. They looked at heart attacks and strokes and death. At the end of the day, that is what really matters.”<br />
</blockquote><br />
<br />
This randomized, open-label clinical trial “assigned 7,447 people in Spain who were overweight, were smokers, or had diabetes or other risk factors for heart disease to follow the Mediterranean diet or a low-fat one.” The low-fat diet was the control and the Mediterranean diet had two arms, one with nuts and the other with extra-virgin olive oil.<br />
<br />
Reproduced below is a graph from the NYT article that highlights the benefits of either form of the Mediterranean diet.<br />
<br />
::[[File:NYT_HeartDiseaseAndDiet.gif]]<br />
<br />
The claim is that “about 30 percent of heart attacks, strokes and deaths from heart disease can be prevented in people at high risk if they switch to a Mediterranean diet.”<br />
<br />
===Discussion===<br />
<br />
1. The NEJM study itself may be found [http://www.nejm.org/doi/full/10.1056/NEJMoa1200303?query=featured_home#t=abstract here]. Its results are stated thusly:<br />
<blockquote><br />
RESULTS<br />
A total of 7447 persons were enrolled (age range, 55 to 80 years); 57% were women. The two Mediterranean-diet groups had good adherence to the intervention, according to self-reported intake and biomarker analyses. A primary end-point event occurred in 288 participants. The multivariable-adjusted hazard ratios were 0.70 (95% confidence interval [CI], 0.54 to 0.92) and 0.72 (95% CI, 0.54 to 0.96) for the group assigned to a Mediterranean diet with extra-virgin olive oil (96 events) and the group assigned to a Mediterranean diet with nuts (83 events), respectively, versus the control group (109 events). No diet-related adverse effects were reported.<br />
</blockquote><br />
2. Here are the explicit recommendations for the Mediterranean diet and the (control) low-fat diet:<br />
<blockquote><br />
'''Mediterranean diet''' <br><br />
''Recommended''<br><br />
:Olive oil(*) ≥4 tbsp/day<br><br />
:Tree nuts and peanuts† ≥3 servings/wk<br><br />
:Fresh fruits ≥3 servings/day<br><br />
:Vegetables ≥2 servings/day<br><br />
:Fish (especially fatty fish), seafood ≥3 servings/wk<br><br />
:Legumes ≥3 servings/wk<br><br />
:Sofrito‡ ≥2 servings/wk<br><br />
:White meat Instead of red meat<br><br />
:Wine with meals (optionally, only for habitual drinkers) ≥7 glasses/wk.<br><br />
''Discouraged''<br><br />
:Soda drinks <1 drink/day<br><br />
:Commercial bakery goods, sweets, and pastries§ <3 servings/wk<br><br />
:Spread fats <1 serving/day<br><br />
:Red and processed meats <1 serving/day<br />
<br><br />
'''Low-fat diet''' (control)<br><br />
''Recommended''<br><br />
:Low-fat dairy products ≥3 servings/day<br><br />
:Bread, potatoes, pasta, rice ≥3 servings/day<br><br />
:Fresh fruits ≥3 servings/day<br><br />
:Vegetables ≥2 servings/wk<br><br />
:Lean fish and seafood ≥3 servings/wk<br><br />
''Discouraged''<br><br />
:Vegetable oils (including olive oil) ≤2 tbsp/day<br><br />
:Commercial bakery goods, sweets, and pastries§ ≤1 serving/wk<br><br />
:Nuts and fried snacks ≤1 serving /wk<br><br />
:Red and processed fatty meats ≤1 serving/wk<br><br />
:Visible fat in meats and soups¶ Always remove<br><br />
:Fatty fish, seafood canned in oil ≤1 serving/wk<br><br />
:Spread fats ≤1 serving/wk<br><br />
:Sofrito‡ ≤2 servings/wk<br />
<br />
(*)The amount of olive oil includes oil used for cooking and salads and oil consumed in meals eaten outside the home. In the group assigned to the Mediterranean diet with extra-virgin olive oil, the goal was to consume 50 g (approximately 4 tbsp) or more per day of the polyphenol-rich olive oil supplied, instead of the ordinary refined variety, which is low in polyphenols.<br><br />
†For participants assigned to the Mediterranean diet with nuts, the recommended consumption was one daily serving (30 g, composed of 15 g of walnuts, 7.5 g of almonds, and 7.5 g of hazelnuts).<br><br />
‡Sofrito is a sauce made with tomato and onion, often including garlic and aromatic herbs, and slowly simmered with olive oil.<br><br />
§ Commercial bakery goods, sweets, and pastries (not homemade) included cakes, cookies, biscuits, and custard.<br><br />
¶Participants were advised to remove the visible fat (or the skin) of chicken, duck, pork, lamb, or veal before cooking and the fat of soups, broths, and cooked meat dishes before consumption. <br />
</blockquote><br />
<br />
3. Why would the above recommendations be difficult to follow in some parts of the world? Google sofrito to see if you have consumed it under another name. Comment on the inexactness of the term “a serving.”<br />
<br />
4. “Peanuts” are part of the recommendations but in the footnote, only walnuts, almonds and hazel nuts appear. Try to come up with an explanation for the exclusion of peanuts.<br />
<br />
5. The Mediterranean while not as large as the Atlantic or the Pacific, does include North Africa as well as many European countries. If your ancestors come from one of those places, comment on how your Mediterranean cuisine heritage might differ from the above recommendations when it comes to cheese, meat, wine, butter, etc.<br />
<br />
6. If strokes, heart attacks and death are more meaningful--as they definitely are--than surrogate criteria such as cholesterol, blood pressure and weight gain, why do so many studies look at surrogate measures only?<br />
<br />
7. This study had 18 authors some of whom served on the board of the Research Foundation on Wine and Nutrition, received support from the California Walnut Commision, the International Nut and Dried Food Council, Nestle, PepsiCo, the Beer and Health Foundation and Danone.<br />
<br />
8. The authors state that “Among persons at high cardiovascular risk, a Mediterranean diet supplemented with extra-virgin olive oil or nuts reduced the incidence of major cardiovascular events.” Why was no conclusion drawn regarding persons who are at low cardiovascular risk?<br />
<br />
9. But maybe the last word on the subject of diets can be found in the NYT article. Dr. Esselstyn, a noted vegan, remarked<br />
<blockquote><br />
those in the Mediterranean diet study still had heart attacks and strokes. So, he said, all the study showed was that “the Mediterranean diet and the horrible control diet were able to create disease in people who otherwise did not have it.”<br />
</blockquote><br />
<br />
10. Let us not forget the famous phrase popular among our forbearers: when it comes to diets, there are really only two: food and no food.<br />
<br />
Submitted by Paul Alper<br />
<br />
==Using genetic data without permission==<br />
<br />
[http://www.nytimes.com/2013/03/24/opinion/sunday/the-immortal-life-of-henrietta-lacks-the-sequel.html The Immortal Life of Henrietta Lacks, the Sequel], Rebecca Skloot, The New York Times, March 23, 2013.<br />
<br />
<p>There is a pompous attitude that some people take when they are told about research abuses. It goes along the lines of "That happened years ago, and with all of today's safeguards, it could never happen again." Well, maybe, but this article provides a healthy reminder that sometimes we don't learn from our mistakes.</p><br />
<br />
<p>Rebecca Skloot wrote an excellent book (The Immortal Life of Henrietta Lacks) about Henrietta Lacks and a line of cells (HeLa) derived from a tumor that killed her in 1951. This book is worth reading if you are involved with research because it deals with the issue of taking tissues from a person and using them for research with getting consent first. It also talks about abuses of Henrietta Lacks's family. Most of the problems occured before we had the Belmont Report and Institutional Review Boards. But recently, researchers, presumably all trained in the proper conduct of research, made a very similar mistake with the same family. Scientists sequenced and published the full genome from the HeLa cell line. They did this without seeking the consent of family members</p><br />
<br />
<p>Research on a dead person normally has few barriers and this is often reasonable. But genetic information is different because it reveals something more. Genetic information provides data not just on the dead person but on any living relatives. This is not universally appreciated, even by experts in the field.</p><br />
<br />
<blockquote>A news release from the European Molecular Biology Laboratory, where the HeLa genome was sequenced, said, "We cannot infer anything about Henrietta Lacks’s genome, or of her descendants, from the data generated in this study."</blockquote><br />
<br />
But this data can be combined with publicly available resources to surprising results. One scientist<br />
<br />
<blockquote>uploaded HeLa’s genome to a public Web site called SNPedia, a Wikipedia-like site for translating genetic information. Minutes later, it produced a report full of personal information about Henrietta Lacks, and her family. (The scientist kept that report confidential, sharing it only with me.) Until recently, few people had the ability to process raw genome data like this. Now anyone who can send an e-mail can do it. No one knows what we may someday learn about Lacks’s great-grandchildren from her genome, but we know this: the view we have today of genomes is like a world map, but Google Street View is coming very soon. </blockquote><br />
<br />
<p>There is a growing awareness in the research community that DNA sequences, in particular, raise difficult issues that are unaddressed by current regulations.</p><br />
<br />
<blockquote>The problem, says Yaniv Erlich, a fellow at the Whitehead Institute for Biomedical Research, is that anonymity vanishes when it comes to DNA: “People don’t realize it’s impossible to hide genetic information once it’s out there.” He and his colleagues recently proved that it’s possible to use online public databases to find the identities of people whose anonymous DNA samples had been sequenced and published online. Yet researchers aren’t required to tell you that there is no guarantee that a genome, once sequenced, will stay private or anonymous. </blockquote><br />
<br />
===Discussion===<br />
<br />
1. What are some of the problems that can occur when genetic information about an individual becomes publicly available?<br />
<br />
2. Would you consider letting your DNA sequence be openly published for the benefit of research? Would you check first with your parents, siblings, or children before doing this?<br />
<br />
3. Can reasonable safeguards be put in place to allow distribution of gene sequence data without compromising privacy?<br />
<br />
Submitted by Steve Simon</div>Simon66217https://www.causeweb.org/wiki/chance/index.php?title=Chance_News_92&diff=17209Chance News 922013-03-27T19:56:11Z<p>Simon66217: /* Discussion */</p>
<hr />
<div>==Quotations==<br />
"I've done the calculation and your chances of winning the lottery are identical whether you play or not."<br />
<div align=right>--Fran Lebowitz (American author and humorist)</div><br />
<br />
Suggested by Naomi Neff (with thanks to Cynthia Slater)<br />
<br />
----<br />
"As much as it pleases me to see statistical data introduced in the Supreme Court, the act of citing statistical factoids is not the same thing as drawing sound inferences from them."<br />
<br />
<div align=right>--Nate Silver, [http://fivethirtyeight.blogs.nytimes.com/2013/03/07/in-supreme-court-debate-on-voting-rights-act-a-dubious-use-of-statistics/ In Supreme Court Debate on Voting Rights Act, a Dubious Use of Statistics] FiveThirtyEight blog</div><br />
<br />
Submitted by Paul Alper<br />
<br />
----<br />
“economisting … 1. The act or process of converting limited evidence into grand claims by means of punning, multiplicity of meaning, and over-reaching. 2. The belief or practice that empirical evidence can only confirm and never disconfirm a favored theory. 3. Conclusions that are theory-driven, not evidence-based.”<br />
<div align=right>Anthropologist Clifford Geertz, <i>Available Light: Anthropological Reflections on Philosophical Topics</i>, Princeton, 2000<br><br />
quoted by Edward Tufte in his [http://www.edwardtufte.com/bboard/q-and-a-fetch-msg?msg_id=0001Zl <i>Beautiful Evidence</i>], Graphics Press, 2006</div><br />
Submitted by Margaret Cibes<br />
<br />
----<br />
"Statistics books almost always illustrate this point by drawing colored marbles out of an urn. (In fact, it's about the only place where one sees the word 'urn' used with any regularity.)<br />
<br />
<div align=right>--Charles Wheelan, ''Naked Statistics'' (p. 112)</div><br />
<br />
Submitted by Paul Alper<br />
<br />
==Forsooth==<br />
Miracles of the loaves and fishes, from <i>The Wall Street Journal</i> ....<br><br />
<br />
“The quants have arrived at the Academy [of Motion Picture Arts and Sciences]. …. The goals in making ... predictions extend beyond [Oscar night]. Dr. Rothschild [Microsoft Research economist] is testing whether surveying people online about Oscar patterns—for example, does winning best-adapted screenplay correspond with winning best picture?—is a method that can be translated to forecasting in other areas. If it works, ‘We can apply it to all sorts of other things we don't have data for,’ Dr. Rothschild said."<br />
<div align=right>Carl Bialik in [http://online.wsj.com/article/SB10001424127887324503204578318682787064790.html?KEYWORDS=carl+bialik#articleTabs%3Darticle “And the Oscar-Pool Winners Are...the Stats Dudes”]<br><br />
by Carl Bialik, February 23, 2013</div><br />
<br />
<center>[[file:Extrapolation.jpg|150px]]</center><br />
<div align=right>[http://online.wsj.com/article/SB10001424127887324077704578358231107272180.html?mg=id-wsj “Big Data Broadens Its Range”], March 13, 2013</div><br />
Submitted by Margaret Cibes<br />
<br />
==Simpson’s paradox and the ecological fallacy==<br />
<br />
The lay public tends to believe that statistics is merely a (rather dull) branch of mathematics. In fact, the discipline of statistics should be viewed as a science, as exemplified by physics, astronomy, chemistry, etc., which uses mathematics extensively and is situation dependent. In other word, the same numbers lead to different conclusion depending on the context.<br />
<br />
Prime examples of situation dependency may be found in the discussions of Simpson’s paradox and the even more subtle phenomenon known as the ecological fallacy. A treatment of the former can sometimes be found in elementary statistics textbooks but the latter, being less intuitive, is relatively rare in textbooks but often pops up in learned discussions where the reader is warned about drawing false conclusions.<br />
<br />
The dating of the phenomenon now known as Simpson’s paradox goes back before any of the current Chance News readers were born; the bestowing of the name, according to [http://en.wikipedia.org/wiki/Simpson's_paradox Wikipedia], originated much later in 1971:<br />
<blockquote><br />
Simpson's paradox (or the Yule–Simpson effect) is a paradox in which a trend [i.e., inequality] that appears in different groups of data disappears when these groups are combined, and the reverse trend [i.e., opposite inequality] appears for the aggregate data. This result is often encountered in social-science and medical-science statistics, and is particularly confounding when frequency data are unduly given causal interpretations.<br />
</blockquote><br />
The Wikipedia article has this “real-life example from a medical study comparing the success rates of two treatments for kidney stones.”<br />
<br />
<table class="wikitable" summary="results accounting for stone size" style="margin-left:auto; margin-right:auto;"><br />
<tr><br />
<th></th><br />
<th>Treatment A</th><br />
<th>Treatment B</th><br />
</tr><br />
<tr align="center"><br />
<th>Small Stones</th><br />
<td><i>Group 1</i><br /><br />
<b>93% (81/87)</b></td><br />
<td><i>Group 2</i><br /><br />
87% (234/270)</td><br />
</tr><br />
<tr align="center"><br />
<th>Large Stones</th><br />
<td><i>Group 3</i><br /><br />
<b>73% (192/263)</b></td><br />
<td><i>Group 4</i><br /><br />
69% (55/80)</td><br />
</tr><br />
<tr align="center"><br />
<th>Both</th><br />
<td>78% (273/350)</td><br />
<td><b>83% (289/350)</b></td><br />
</tr><br />
</table><br />
<blockquote><br />
The paradoxical conclusion is that treatment A is more effective when used on small stones, [93% > 87%] and also when used on large stones, [73% > 69%] yet treatment B is more effective when considering both sizes at the same time [78% < 83%]. In this example, the "lurking" variable (or confounding variable) of the stone size was not previously known to be important until its effects were included.<br />
</blockquote><br />
In this context of kidney stones, it is clear that disaggregation makes sense and Treatment A is preferable to Treatment B despite Treatment B being better in the aggregate sense. However, if we take the same numbers but change the context to Athletic Team A and Athletic Team B who play Small and Large opponents and the only thing that determines ranking is the total winning percentage, then Athletic Team B is preferred to Athletic Team A. That is, aggregation makes sense in this scenario as it did not in the original Wikipedia presentation. <br />
<br />
Other interesting examples are provided in the Wikipedia article. When money is at stake, as in the “Berkeley gender bias” case discussed in Wikipedia, finding a lurking (confounding) variable requires some clever slicing to find “Small” and “Large” which will reverse the inequality. The Wikipedia article also refers to the so-called “low birth rate paradox” whereby “it has been observed that babies of low birth weights born to smoking mothers have a lower mortality rate than the babies of low birth weights of non-smokers.” The paradoxical implication is that smoking helps to lower mortality of newborns. More on this below.<br />
<br />
[http://en.wikipedia.org/wiki/Ecological_fallacy A different Wikipedia article] has two definitions of the ecological fallacy. The first definition focuses on aggregation and disaggregation. With this definition, Simpson’s paradox is subsumed under the ecological fallacy:<br />
<blockquote><br />
An ecological fallacy (or ecological inference fallacy) is a logical fallacy in the interpretation of statistical data where inferences about the nature of individuals [disaggregation] are deduced from inference for the group [aggregation] to which those individuals belong. <br />
</blockquote><br />
The second definition spotlights the notion of correlation:<br />
<blockquote><br />
<br />
Ecological fallacy can refer to the following statistical fallacy: the correlation between individual variables is deduced from the correlation of the variables collected for the group to which those individuals belong.<br />
</blockquote><br />
<br />
Although elementary statistics textbooks do not customarily mention the ecological fallacy, it is even older than Simpson’s paradox. The term was first coined in 1950 by William Robinson but goes back to Emile Durkheim’s 1897 study of suicide. [http://www.teachepi.org/documents/courses/bfiles/The%20B%20Files_File3_Durkheim_Final_Complete.pdf From the graph], it appears that the greater the proportion of Protestants, the greater is the suicide rate:<br />
<br />
<center>[[File:Suicide.png | 600px]]</center><br />
<br />
<blockquote><br />
According to Morgenstern, the estimated rate ratio of 7.6 was probably not because suicide rates were nearly 8 fold higher in Protestants than in non-Protestants. Rather, because none of the regions was entirely Protestant or non-Protestant, it may have been non-Protestants (primarily Catholics) who were committing suicide in predominantly Protestant provinces. It is plausible that members of a religious minority might have been more likely to commit suicide than were members of the majority. Living in a <br />
predominantly Protestant area had a contextual effect on suicide risk among Catholics.<br />
<br><br><br />
Interestingly, Morgenstern points out that Durkheim compared the suicide rates at the individual level for Protestants, Catholics and Jews living in Prussia, and from his data, the rate was about twice as great in Protestants as in other religious groups. Thus, when the rate ratios are compared (2 vs 8), there appears to be substantial ecological bias using the aggregate level data.<br />
</blockquote><br />
<br />
In the above situation there was no reversal of an inequality, merely a sharp diminishing from aggregated to disaggregated. The following example of the ecological fallacy actually illustrates the reversal. <br />
<blockquote><br />
<br />
One compelling example by Robinson (1950), was the relationship between nativity (foreign vs native born) and literacy. For each of the 48 states in the USA of 1930, [there were only 48 states admitted to the Union by 1930] Robinson computed two numbers: the percent of the population who were foreign-born (i.e. immigrants), and the percent who were literate. He found the correlation between the 48 pairs of numbers was .53. This ecological correlation suggested a positive association between foreign birth and literacy: the foreign-born (immigrants) are more likely to be literate than the native-born. In reality, the association was negative: the correlation computed at the individual level was −0.11 (immigrants were less literate than native citizens). The ecological correlation gave the incorrect inference. This is because the foreign-born (immigrants) tended to migrate to and settle in states where the native-born are relatively literate. In this example by Robinson, the correlation is totally reversed. <br />
</blockquote><br />
<br />
[http://ije.oxfordjournals.org/content/40/4/1123.full Robinson’s data] look this way:<br />
<br />
<center> [[File:Robinson.png | 450 px]] </center><br />
<br />
[http://blog.statwing.com/the-ecological-fallacy/ The following graph] dealing this time with income and being foreign born is even more striking:<br />
<br />
<br />
<center> [[File:Income.png | 450 px]] </center><br />
<blockquote><br />
U.S. states with proportionally more immigrants have proportionally more households with income above $100k. Ergo, immigrants are more likely than non-immigrants to have household incomes above $100k.<br />
<br><br><br />
Hopefully something feels off about that logic. Because it’s wrong. Actually the relationship between income and being an immigrant at the individual level is the opposite.<br />
<br><br><br />
<center> [[File:Foreign-Born-vs-Income-Indiv.png | 250 px]] </center><br />
Deducing from the first chart that immigrants are more likely to be well-off is committing the ecological fallacy—attributing qualities at the individual level because of a relationship at a group level.<br />
</blockquote><br />
But here is a more recent and more difficult-to-unravel ecological fallacy:<br />
<br />
<blockquote><br />
That example was pretty easy to catch, not least because it feels intuitive that immigrants would tend to have lower income than non-immigrants. <br />
<br><br><br />
But not all ecological fallacies are so easy to spot.<br />
For example, there’s a negative correlation between per capita income in a state and the percent of the 2012 presidential election vote that went to Romney.<br />
<center> [[File: Income-vs-Republican.png| 450 px]] </center><br />
<br><br><br />
It’s easy to picture rich and liberal cities like San Francisco and New York, hear the phrase “latte liberal” a couple times, and believe that higher income is in fact correlated with voting Democratic.<br />
At an individual level, though, higher income is associated with voting Republican.<br />
<center> [[File: Republican-Vote-Share.jpg | 350 px]] </center><br />
The (simplified) explanation for this apparent paradox? Across the country, lower income folk tend to vote Democrat; within blue states, upper income folk also vote Democrat, but in red states they vote Republican. <br />
</blockquote><br />
<br />
A general way to look at where the fallacy might arise is via the [http://www.teachepi.org/documents/courses/bfiles/The%20B%20Files_File3_Durkheim_Final_Complete.pdf following graph and explanation of Durkheim’s suicide data]:<br />
<br />
<center>[[File:Dirkheim_expl.png | 600px]]</center><br />
<br />
That is, within every group it is possible that even if the correlation (regression line) is negative, it can happen that across the groups, the correlation (regression line) is positive. Note too that in many situations the “within” is not a cloud of points, each of which represents an individual, but instead, there is just one point, average exposure and average outcome. Further, exposure may come from one data base and outcome from another data base. This is totally unlike the kidney stones example which began this wiki because there stone and success can be tied to a particular individual. <br />
<br />
===Discussion===<br />
<br />
1. An oft-used synonym for the ecological fallacy (inferring from group to individuals) is called cross level inference. The opposite of the ecological fallacy is the atomistic fallacy (inferring from the individuals to the group).<br />
<br />
2. With regard to Robinson’s data, besides the fallacy aspect, what is wrong with doing a correlation in the first place?<br />
<br />
3. Concerning the graph of foreign born and income, suppose the ordinates were interchanged. How is this then similar to Durkheim’s study and its ecological fallacy?<br />
<br />
4. [http://en.wikipedia.org/wiki/Low_birth_weight_paradox The paradox of the smoking mother] is supposedly explained by the following:<br />
<blockquote><br />
The birth weight distribution for children of smoking mothers is shifted to lower weights by their mothers' actions. Therefore, otherwise healthy babies (who would weigh more if it were not for the fact their mother smoked) are born underweight. They have a lower mortality rate than children who have other medical reasons why they are born underweight, regardless of the fact their mother does not smoke.<br />
In short, smoking may be harmful in that it contributes to low birth weight, but other causes of low birth weight are generally more harmful only with regard to their weight.<br />
</blockquote><br />
How does this explanation accord with the aforementioned phrase, situation dependent?<br />
<br />
Submitted by Paul Alper<br />
<br />
==Normal vs. paranormal==<br />
John Allen Paulos sent a link to the following cartoon, reproduced below as presented on the StackExchange blog [http://stats.stackexchange.com/posts/14356/revisions Cross Validated]:<br />
<center>[[File:T2XrE.gif]]<br />
<br>From: '''A visual comparison of normal and paranormal distributions'''<br> Matthew Freeman ''J Epidemiol Community Health'' 2006;60:6. <br>Lower caption says 'Paranormal Distribution'- no idea why the graphical artifact is occuring.<br />
</center><br />
<br />
==Gallup reviewing its methods==<br />
[http://www.huffingtonpost.com/2013/03/08/gallup-presidential-poll_n_2806361.html “Gallup Presidential Poll: How Did Brand-Name Firm Blow Election?”]<br><br />
<i>HuffPost Pollster</i>, March 8, 2013<br><br />
<br />
The article discusses Gallup’s consistently favorable-to-Romney poll results over the Fall 2012 presidential election cycle, including a final Romney 49%-Obama 48% result. (Of course, 49 to 48 does not a winning prediction make.) It includes a nice scatterplot illustrating that the Gallup results deviated remarkably (not necessarily “significantly”) from other national polls over this period.<br><br />
<br />
Apparently Gallup revised its methodology re presidential approval polling in October 2012, in order to correct an “under-representation of non-whites in its samples.” Another nice scatterplot shows how Gallup’s rating results moved more into line with other polls’ results in October of the period July 2012-January 2013.<br><br />
<br />
The article also contains a somewhat detailed discussion of two serious problems facing Gallup and other pollsters today: “how they treat their ‘likely voter’ models and how they draw their samples from the general population.” These are issues associated with identifying likely voters and with reaching them by phone.<br><br />
<br />
Submitted by Margaret Cibes<br />
<br />
==Naked Statistics== <br />
<br />
Charles Wheelan’s book, ''Naked Statistics: Stripping the Dread from the Data'', is a breezy fun-filled read, his “homage to an earlier W.W. Norton classic, ''How to Lie with Statistics'' by Daryll Huff. Without my wishing to imply anything negative, a few decades back, ''Naked Statistics'' would be an ideal text for a course entitled, “Statistics for Poets.” Today, even poets, drama students, and people whose specialty is 17th century French drama (perhaps unfortunately) really need to learn some basic statistics. From the very Introduction to the book, he emphasizes his distaste for mathematics for mathematics sake: “What is the area beneath a parabola? Who Cares?” Yet, he likes physics which uses the same math “Because physics has a clear purpose.” Likewise, “I love statistics,” a comment not often seen or heard outside of Chance News. <br />
<br />
As he puts it<br />
<blockquote><br />
The paradox of statistics is that they are everywhere--from batting averages to presidential polls--but the discipline itself has a reputation for being uninteresting and inaccessible. Many statistics books and classes are overly laden with math and jargon. Believe me, the technical details are crucial (and interesting)--but it’s just Greek if you don’t understand the intuition. And you may not even care about the intuition if you’re not convinced that there is any reason to learn it. Every chapter in this book promises to answer the basic question that I asked (to no effect) of my high school calculus teacher: What is the point of this?<br />
<br><br><br />
The point is that statistics helps process data, which is really just a fancy name for information.<br />
</blockquote><br />
<br />
His motto is “Statistics can be really interesting, and most of it isn’t that difficult.” By the end of the book the reader is confronting regression analysis, which he calls “the miracle elixir, and in the next chapter, why it may not be. His examples vary from the amusingly bizarre to the downright practical. ''Naked Statistics'' is an ideal gift to a significant other who loves you but wonders about what you actually do with your time.<br />
<br />
'''Discussion'''<br />
<br />
1. On page xii he reveals “a career epiphany” he had at math camp. The math teacher was describing without any physical context that the infinite (geometric) series <br />
1+1/2 + 1/4 + 1/8 +…converges to a finite number. Wheelan came up with the following context to make it meaningful to him: A wall is two feet away and your first move is one foot, followed by a move of 1/2 foot, followed by a move of 1/4 foot and so on until you are “pretty darn close to the wall.” What would happen to you and the wall if the infinite series was instead 1+1/2 + 1/3 +1/4 + 1/5 +1/6 + 1/7 +1/8 +…?<br />
<br />
2. Nate Silver’s book, ''The Signal and the Noise'', is a hymn to Bayesian statistics. ‘’Naked Statistics’’ has no mention whatever of Bayes or Silver so that your significant other will have to do some outside reading. Wheelan promises that his second edition will include Bayesian concepts.<br />
<br />
3. [http://www.nytimes.com/2013/01/29/science/naked-statistics-by-charles-wheelan-review.html The review in the ''NYT''] put it this way: <br />
<br />
<blockquote><br />
While a great measure of the book’s appeal comes from Mr. Wheelan’s fluent style — a natural comedian, he is truly the Dave Barry of the coin toss set — the rest comes from his multiple real world examples illustrating exactly why even the most reluctant mathophobe is well advised to achieve a personal understanding of the statistical underpinnings of life, whether that individual is watching football on the couch, picking a school for the children or jiggling anxiously in a hospital admitting office.<br><br><br />
Are you a fan of those handy ranking systems based on performance data, guaranteed to steer you to the best surgeons in town? If so, you are up to your armpits in descriptive statistics, and Mr. Wheelan has some advice for you: beware. The easiest way for doctors to game those numbers is by avoiding the sickest patients.<br />
</blockquote><br />
<br />
How do college football and basketball teams similarly game the numbers?<br />
<br />
4. At the same ''NYT'' review there is an accompanying graphic taken from Wheelan’s book:<br />
<center>[[File:NYT_29scibooks-graphic-popup.jpg | 400px]]</center><br />
From the graphic, why would a (Pearson product-moment) correlation be misleading? Why the “reverse causality”?<br />
<br />
[Note: The ''NYT'' also provided [http://graphics8.nytimes.com/packages/pdf/science/naked-stats-excerpt.pdf this excerpt] from the book's introductory chapter.]<br />
<br />
Submitted by Paul Alper<br />
<br />
==Miscellaneous stats news==<br />
From <i>The Wall Street Journal</i>:<br><br />
<br />
"One [issue] is, if we see a sequence of words, how can we best guess which word is likely to come next. …. The other is how does that relate to the way a user actually interacts with their [sic] touch screen. The way we do this is essentially by modeling the surface of the keyboard as a series of probability distributions. What that means in layman’s terms is, the keyboard looks a bit like a mountain range with a peak where the user perceives each of the keys to be. We collect the points that you touch the screen, and we form and mold the mountains around those points. That gives us a unique snapshot of the way you perceive your keyboard. If we solve that problem, that gives us probabilities we can also use with the language probabilities we have, and then we tie these things together. What comes out at the end is the solution to this central mathematical problem — how do I guess what the user is trying to say.”<br />
<div align=right>Tech officer for Android in [http://blogs.wsj.com/digits/2013/03/18/the-science-behind-guessing-what-youll-type-next/?KEYWORDS=from+a+molehill+to+a+mountain+-+graphic “The Science Behind What You’ll Type Next”], March 18, 2013</div><br />
<br />
“The training of data scientists hasn't caught up with that demand, leaving companies searching for talent and especially, some say, for the relatively few people with extensive experience in the field. …. Tech workers with a full complement of big-data analysis skills are paid on average 11.5% more than people without those skills ….”<br />
<div align=right>[http://online.wsj.com/article/SB10001424127887323549204578319930378618430.html?KEYWORDS=spencer+e+ante “Help Wanted! Data, data everywhere – and not enough people to decipher it”], March 8, 2013</div><br />
<br />
“Poring once more over a 12-year-old set of data on breast-cancer tumors, Dr. Lum saw correlations between the disease and patients' outcomes that she and her fellow researchers had never noticed before …. Dr. Lum's new view came courtesy of software that uses topology, a branch of math that compresses relationships in complex data into shapes researchers can manipulate and probe: in this case, a Y, like a two-eared worm. …. [R]esearchers increasingly are scouring scientific papers and esoteric branches of mathematics like topology to make sense of complex data sets. …. Using graph theory, a tool similar to topology, IBM is mapping interactions of people on social networks, including its own.”<br />
<div align=right>[http://online.wsj.com/article/SB10001424127887323452204578288264046780392.html?KEYWORDS=deborah+gage “The New Shape of Big Data”], March 8, 2013</div><br />
<br />
Also so [http://online.wsj.com/article/SB10001424127887324196204578298381588348290.html?KEYWORDS=shira+ovide “Big Data, Big Blunders”], March 8, 2013<br><br />
<br />
Submitted by Margaret Cibes<br />
==Mediterranean diet==<br />
[http://www.nytimes.com/2013/02/26/health/mediterranean-diet-can-cut-heart-disease-study-finds.html?pagewanted=all&_r=1& Mediterranean diet shown to ward off heart attack and stroke]<br><br />
by Gina Kolata, ''New York Times'', 25 February 2013<br />
<br />
Diets come and diets go: high protein, Atkins, South Beach, Dash, Weight Watchers, low carb, no carb. But then there is the perennial favorite, the so-called Mediterranean diet which has generated some recent positive publicity. According to the NYT article:<br />
<blockquote><br />
The findings, published on The New England Journal of Medicine’s Web site on Monday, were based on the first major clinical trial to measure the diet’s effect on heart risks. The magnitude of the diet’s benefits startled experts. The study ended early, after almost five years, because the results were so clear it was considered unethical to continue.<br />
</blockquote><br />
According to someone who was not connected with this study conducted from Spain,<br />
<blockquote><br />
“And the really important thing — the coolest thing — is that they used very meaningful endpoints. They did not look at risk factors like cholesterol or hypertension or weight. They looked at heart attacks and strokes and death. At the end of the day, that is what really matters.”<br />
</blockquote><br />
<br />
This randomized, open-label clinical trial “assigned 7,447 people in Spain who were overweight, were smokers, or had diabetes or other risk factors for heart disease to follow the Mediterranean diet or a low-fat one.” The low-fat diet was the control and the Mediterranean diet had two arms, one with nuts and the other with extra-virgin olive oil.<br />
<br />
Reproduced below is a graph from the NYT article that highlights the benefits of either form of the Mediterranean diet.<br />
<br />
::[[File:NYT_HeartDiseaseAndDiet.gif]]<br />
<br />
The claim is that “about 30 percent of heart attacks, strokes and deaths from heart disease can be prevented in people at high risk if they switch to a Mediterranean diet.”<br />
<br />
===Discussion===<br />
<br />
1. The NEJM study itself may be found [http://www.nejm.org/doi/full/10.1056/NEJMoa1200303?query=featured_home#t=abstract here]. Its results are stated thusly:<br />
<blockquote><br />
RESULTS<br />
A total of 7447 persons were enrolled (age range, 55 to 80 years); 57% were women. The two Mediterranean-diet groups had good adherence to the intervention, according to self-reported intake and biomarker analyses. A primary end-point event occurred in 288 participants. The multivariable-adjusted hazard ratios were 0.70 (95% confidence interval [CI], 0.54 to 0.92) and 0.72 (95% CI, 0.54 to 0.96) for the group assigned to a Mediterranean diet with extra-virgin olive oil (96 events) and the group assigned to a Mediterranean diet with nuts (83 events), respectively, versus the control group (109 events). No diet-related adverse effects were reported.<br />
</blockquote><br />
2. Here are the explicit recommendations for the Mediterranean diet and the (control) low-fat diet:<br />
<blockquote><br />
'''Mediterranean diet''' <br><br />
''Recommended''<br><br />
:Olive oil(*) ≥4 tbsp/day<br><br />
:Tree nuts and peanuts† ≥3 servings/wk<br><br />
:Fresh fruits ≥3 servings/day<br><br />
:Vegetables ≥2 servings/day<br><br />
:Fish (especially fatty fish), seafood ≥3 servings/wk<br><br />
:Legumes ≥3 servings/wk<br><br />
:Sofrito‡ ≥2 servings/wk<br><br />
:White meat Instead of red meat<br><br />
:Wine with meals (optionally, only for habitual drinkers) ≥7 glasses/wk.<br><br />
''Discouraged''<br><br />
:Soda drinks <1 drink/day<br><br />
:Commercial bakery goods, sweets, and pastries§ <3 servings/wk<br><br />
:Spread fats <1 serving/day<br><br />
:Red and processed meats <1 serving/day<br />
<br><br />
'''Low-fat diet''' (control)<br><br />
''Recommended''<br><br />
:Low-fat dairy products ≥3 servings/day<br><br />
:Bread, potatoes, pasta, rice ≥3 servings/day<br><br />
:Fresh fruits ≥3 servings/day<br><br />
:Vegetables ≥2 servings/wk<br><br />
:Lean fish and seafood ≥3 servings/wk<br><br />
''Discouraged''<br><br />
:Vegetable oils (including olive oil) ≤2 tbsp/day<br><br />
:Commercial bakery goods, sweets, and pastries§ ≤1 serving/wk<br><br />
:Nuts and fried snacks ≤1 serving /wk<br><br />
:Red and processed fatty meats ≤1 serving/wk<br><br />
:Visible fat in meats and soups¶ Always remove<br><br />
:Fatty fish, seafood canned in oil ≤1 serving/wk<br><br />
:Spread fats ≤1 serving/wk<br><br />
:Sofrito‡ ≤2 servings/wk<br />
<br />
(*)The amount of olive oil includes oil used for cooking and salads and oil consumed in meals eaten outside the home. In the group assigned to the Mediterranean diet with extra-virgin olive oil, the goal was to consume 50 g (approximately 4 tbsp) or more per day of the polyphenol-rich olive oil supplied, instead of the ordinary refined variety, which is low in polyphenols.<br><br />
†For participants assigned to the Mediterranean diet with nuts, the recommended consumption was one daily serving (30 g, composed of 15 g of walnuts, 7.5 g of almonds, and 7.5 g of hazelnuts).<br><br />
‡Sofrito is a sauce made with tomato and onion, often including garlic and aromatic herbs, and slowly simmered with olive oil.<br><br />
§ Commercial bakery goods, sweets, and pastries (not homemade) included cakes, cookies, biscuits, and custard.<br><br />
¶Participants were advised to remove the visible fat (or the skin) of chicken, duck, pork, lamb, or veal before cooking and the fat of soups, broths, and cooked meat dishes before consumption. <br />
</blockquote><br />
<br />
3. Why would the above recommendations be difficult to follow in some parts of the world? Google sofrito to see if you have consumed it under another name. Comment on the inexactness of the term “a serving.”<br />
<br />
4. “Peanuts” are part of the recommendations but in the footnote, only walnuts, almonds and hazel nuts appear. Try to come up with an explanation for the exclusion of peanuts.<br />
<br />
5. The Mediterranean while not as large as the Atlantic or the Pacific, does include North Africa as well as many European countries. If your ancestors come from one of those places, comment on how your Mediterranean cuisine heritage might differ from the above recommendations when it comes to cheese, meat, wine, butter, etc.<br />
<br />
6. If strokes, heart attacks and death are more meaningful--as they definitely are--than surrogate criteria such as cholesterol, blood pressure and weight gain, why do so many studies look at surrogate measures only?<br />
<br />
7. This study had 18 authors some of whom served on the board of the Research Foundation on Wine and Nutrition, received support from the California Walnut Commision, the International Nut and Dried Food Council, Nestle, PepsiCo, the Beer and Health Foundation and Danone.<br />
<br />
8. The authors state that “Among persons at high cardiovascular risk, a Mediterranean diet supplemented with extra-virgin olive oil or nuts reduced the incidence of major cardiovascular events.” Why was no conclusion drawn regarding persons who are at low cardiovascular risk?<br />
<br />
9. But maybe the last word on the subject of diets can be found in the NYT article. Dr. Esselstyn, a noted vegan, remarked<br />
<blockquote><br />
those in the Mediterranean diet study still had heart attacks and strokes. So, he said, all the study showed was that “the Mediterranean diet and the horrible control diet were able to create disease in people who otherwise did not have it.”<br />
</blockquote><br />
<br />
10. Let us not forget the famous phrase popular among our forbearers: when it comes to diets, there are really only two: food and no food.<br />
<br />
Submitted by Paul Alper<br />
<br />
==Using genetic data without permission==<br />
<br />
[http://www.nytimes.com/2013/03/24/opinion/sunday/the-immortal-life-of-henrietta-lacks-the-sequel.html The Immortal Life of Henrietta Lacks, the Sequel], Rebecca Skloot, The New York Times, March 23, 2013.<br />
<br />
<p>There is a pompous attitude that some people take when they are told about research abuses. It goes along the lines of "That happened years ago, and with all of today's safeguards, it could never happen again." Well, maybe, but this article provides a healthy reminder that sometimes we don't learn from our mistakes.</p><br />
<br />
<p>Rebecca Skloot wrote an excellent book (The Immortal Life of Henrietta Lacks) about Henrietta Lacks and a line of cells (HeLa) derived from a tumor that killed her in 1951. This book is worth reading if you are involved with research because it deals with the issue of taking tissues from a person and using them for research with getting consent first. It also talks about abuses of Henrietta Lacks's family. Most of the problems occured before we had the Belmont Report and Institutional Review Boards. But recently, researchers, presumably all trained in the proper conduct of research, made a very similar mistake with the same family. Scientists sequenced and published the full genome from the HeLa cell line. They did this without seeking the consent of family members</p><br />
<br />
<p>Research on a dead person normally has few barriers and this is often reasonable. But genetic information is different because it reveals something more. Genetic information provides data not just on the dead person but on any living relatives. This is not universally appreciated, even by experts in the field.</p><br />
<br />
<blockquote>A news release from the European Molecular Biology Laboratory, where the HeLa genome was sequenced, said, "We cannot infer anything about Henrietta Lacks’s genome, or of her descendants, from the data generated in this study."</blockquote><br />
<br />
But this data can be combined with publicly available resources to surprising results. One scientist<br />
<br />
<blockquote>uploaded HeLa’s genome to a public Web site called SNPedia, a Wikipedia-like site for translating genetic information. Minutes later, it produced a report full of personal information about Henrietta Lacks, and her family. (The scientist kept that report confidential, sharing it only with me.) Until recently, few people had the ability to process raw genome data like this. Now anyone who can send an e-mail can do it. No one knows what we may someday learn about Lacks’s great-grandchildren from her genome, but we know this: the view we have today of genomes is like a world map, but Google Street View is coming very soon. </blockquote><br />
<br />
<p>There is a growing awareness in the research community that DNA sequences, in particular, raise difficult issues that are unaddressed by current regulations.</p><br />
<br />
<blockquote>The problem, says Yaniv Erlich, a fellow at the Whitehead Institute for Biomedical Research, is that anonymity vanishes when it comes to DNA: “People don’t realize it’s impossible to hide genetic information once it’s out there.” He and his colleagues recently proved that it’s possible to use online public databases to find the identities of people whose anonymous DNA samples had been sequenced and published online. Yet researchers aren’t required to tell you that there is no guarantee that a genome, once sequenced, will stay private or anonymous. </blockquote><br />
<br />
===Discussion===<br />
<br />
1. What are some of the problems that can occur when genetic information about an individual becomes publicly available?<br />
<br />
2. Would you consider letting your DNA sequence be openly published for the benefit of research? Would you check first with your parents, siblings, or children before doing this?<br />
<br />
3. Can reasonable safeguards be put in place to allow distribution of gene sequence data without compromising privacy?<br />
<br />
Written by Steve Simon</div>Simon66217https://www.causeweb.org/wiki/chance/index.php?title=Chance_News_92&diff=17208Chance News 922013-03-27T19:55:48Z<p>Simon66217: /* Using genetic data without permission */</p>
<hr />
<div>==Quotations==<br />
"I've done the calculation and your chances of winning the lottery are identical whether you play or not."<br />
<div align=right>--Fran Lebowitz (American author and humorist)</div><br />
<br />
Suggested by Naomi Neff (with thanks to Cynthia Slater)<br />
<br />
----<br />
"As much as it pleases me to see statistical data introduced in the Supreme Court, the act of citing statistical factoids is not the same thing as drawing sound inferences from them."<br />
<br />
<div align=right>--Nate Silver, [http://fivethirtyeight.blogs.nytimes.com/2013/03/07/in-supreme-court-debate-on-voting-rights-act-a-dubious-use-of-statistics/ In Supreme Court Debate on Voting Rights Act, a Dubious Use of Statistics] FiveThirtyEight blog</div><br />
<br />
Submitted by Paul Alper<br />
<br />
----<br />
“economisting … 1. The act or process of converting limited evidence into grand claims by means of punning, multiplicity of meaning, and over-reaching. 2. The belief or practice that empirical evidence can only confirm and never disconfirm a favored theory. 3. Conclusions that are theory-driven, not evidence-based.”<br />
<div align=right>Anthropologist Clifford Geertz, <i>Available Light: Anthropological Reflections on Philosophical Topics</i>, Princeton, 2000<br><br />
quoted by Edward Tufte in his [http://www.edwardtufte.com/bboard/q-and-a-fetch-msg?msg_id=0001Zl <i>Beautiful Evidence</i>], Graphics Press, 2006</div><br />
Submitted by Margaret Cibes<br />
<br />
----<br />
"Statistics books almost always illustrate this point by drawing colored marbles out of an urn. (In fact, it's about the only place where one sees the word 'urn' used with any regularity.)<br />
<br />
<div align=right>--Charles Wheelan, ''Naked Statistics'' (p. 112)</div><br />
<br />
Submitted by Paul Alper<br />
<br />
==Forsooth==<br />
Miracles of the loaves and fishes, from <i>The Wall Street Journal</i> ....<br><br />
<br />
“The quants have arrived at the Academy [of Motion Picture Arts and Sciences]. …. The goals in making ... predictions extend beyond [Oscar night]. Dr. Rothschild [Microsoft Research economist] is testing whether surveying people online about Oscar patterns—for example, does winning best-adapted screenplay correspond with winning best picture?—is a method that can be translated to forecasting in other areas. If it works, ‘We can apply it to all sorts of other things we don't have data for,’ Dr. Rothschild said."<br />
<div align=right>Carl Bialik in [http://online.wsj.com/article/SB10001424127887324503204578318682787064790.html?KEYWORDS=carl+bialik#articleTabs%3Darticle “And the Oscar-Pool Winners Are...the Stats Dudes”]<br><br />
by Carl Bialik, February 23, 2013</div><br />
<br />
<center>[[file:Extrapolation.jpg|150px]]</center><br />
<div align=right>[http://online.wsj.com/article/SB10001424127887324077704578358231107272180.html?mg=id-wsj “Big Data Broadens Its Range”], March 13, 2013</div><br />
Submitted by Margaret Cibes<br />
<br />
==Simpson’s paradox and the ecological fallacy==<br />
<br />
The lay public tends to believe that statistics is merely a (rather dull) branch of mathematics. In fact, the discipline of statistics should be viewed as a science, as exemplified by physics, astronomy, chemistry, etc., which uses mathematics extensively and is situation dependent. In other word, the same numbers lead to different conclusion depending on the context.<br />
<br />
Prime examples of situation dependency may be found in the discussions of Simpson’s paradox and the even more subtle phenomenon known as the ecological fallacy. A treatment of the former can sometimes be found in elementary statistics textbooks but the latter, being less intuitive, is relatively rare in textbooks but often pops up in learned discussions where the reader is warned about drawing false conclusions.<br />
<br />
The dating of the phenomenon now known as Simpson’s paradox goes back before any of the current Chance News readers were born; the bestowing of the name, according to [http://en.wikipedia.org/wiki/Simpson's_paradox Wikipedia], originated much later in 1971:<br />
<blockquote><br />
Simpson's paradox (or the Yule–Simpson effect) is a paradox in which a trend [i.e., inequality] that appears in different groups of data disappears when these groups are combined, and the reverse trend [i.e., opposite inequality] appears for the aggregate data. This result is often encountered in social-science and medical-science statistics, and is particularly confounding when frequency data are unduly given causal interpretations.<br />
</blockquote><br />
The Wikipedia article has this “real-life example from a medical study comparing the success rates of two treatments for kidney stones.”<br />
<br />
<table class="wikitable" summary="results accounting for stone size" style="margin-left:auto; margin-right:auto;"><br />
<tr><br />
<th></th><br />
<th>Treatment A</th><br />
<th>Treatment B</th><br />
</tr><br />
<tr align="center"><br />
<th>Small Stones</th><br />
<td><i>Group 1</i><br /><br />
<b>93% (81/87)</b></td><br />
<td><i>Group 2</i><br /><br />
87% (234/270)</td><br />
</tr><br />
<tr align="center"><br />
<th>Large Stones</th><br />
<td><i>Group 3</i><br /><br />
<b>73% (192/263)</b></td><br />
<td><i>Group 4</i><br /><br />
69% (55/80)</td><br />
</tr><br />
<tr align="center"><br />
<th>Both</th><br />
<td>78% (273/350)</td><br />
<td><b>83% (289/350)</b></td><br />
</tr><br />
</table><br />
<blockquote><br />
The paradoxical conclusion is that treatment A is more effective when used on small stones, [93% > 87%] and also when used on large stones, [73% > 69%] yet treatment B is more effective when considering both sizes at the same time [78% < 83%]. In this example, the "lurking" variable (or confounding variable) of the stone size was not previously known to be important until its effects were included.<br />
</blockquote><br />
In this context of kidney stones, it is clear that disaggregation makes sense and Treatment A is preferable to Treatment B despite Treatment B being better in the aggregate sense. However, if we take the same numbers but change the context to Athletic Team A and Athletic Team B who play Small and Large opponents and the only thing that determines ranking is the total winning percentage, then Athletic Team B is preferred to Athletic Team A. That is, aggregation makes sense in this scenario as it did not in the original Wikipedia presentation. <br />
<br />
Other interesting examples are provided in the Wikipedia article. When money is at stake, as in the “Berkeley gender bias” case discussed in Wikipedia, finding a lurking (confounding) variable requires some clever slicing to find “Small” and “Large” which will reverse the inequality. The Wikipedia article also refers to the so-called “low birth rate paradox” whereby “it has been observed that babies of low birth weights born to smoking mothers have a lower mortality rate than the babies of low birth weights of non-smokers.” The paradoxical implication is that smoking helps to lower mortality of newborns. More on this below.<br />
<br />
[http://en.wikipedia.org/wiki/Ecological_fallacy A different Wikipedia article] has two definitions of the ecological fallacy. The first definition focuses on aggregation and disaggregation. With this definition, Simpson’s paradox is subsumed under the ecological fallacy:<br />
<blockquote><br />
An ecological fallacy (or ecological inference fallacy) is a logical fallacy in the interpretation of statistical data where inferences about the nature of individuals [disaggregation] are deduced from inference for the group [aggregation] to which those individuals belong. <br />
</blockquote><br />
The second definition spotlights the notion of correlation:<br />
<blockquote><br />
<br />
Ecological fallacy can refer to the following statistical fallacy: the correlation between individual variables is deduced from the correlation of the variables collected for the group to which those individuals belong.<br />
</blockquote><br />
<br />
Although elementary statistics textbooks do not customarily mention the ecological fallacy, it is even older than Simpson’s paradox. The term was first coined in 1950 by William Robinson but goes back to Emile Durkheim’s 1897 study of suicide. [http://www.teachepi.org/documents/courses/bfiles/The%20B%20Files_File3_Durkheim_Final_Complete.pdf From the graph], it appears that the greater the proportion of Protestants, the greater is the suicide rate:<br />
<br />
<center>[[File:Suicide.png | 600px]]</center><br />
<br />
<blockquote><br />
According to Morgenstern, the estimated rate ratio of 7.6 was probably not because suicide rates were nearly 8 fold higher in Protestants than in non-Protestants. Rather, because none of the regions was entirely Protestant or non-Protestant, it may have been non-Protestants (primarily Catholics) who were committing suicide in predominantly Protestant provinces. It is plausible that members of a religious minority might have been more likely to commit suicide than were members of the majority. Living in a <br />
predominantly Protestant area had a contextual effect on suicide risk among Catholics.<br />
<br><br><br />
Interestingly, Morgenstern points out that Durkheim compared the suicide rates at the individual level for Protestants, Catholics and Jews living in Prussia, and from his data, the rate was about twice as great in Protestants as in other religious groups. Thus, when the rate ratios are compared (2 vs 8), there appears to be substantial ecological bias using the aggregate level data.<br />
</blockquote><br />
<br />
In the above situation there was no reversal of an inequality, merely a sharp diminishing from aggregated to disaggregated. The following example of the ecological fallacy actually illustrates the reversal. <br />
<blockquote><br />
<br />
One compelling example by Robinson (1950), was the relationship between nativity (foreign vs native born) and literacy. For each of the 48 states in the USA of 1930, [there were only 48 states admitted to the Union by 1930] Robinson computed two numbers: the percent of the population who were foreign-born (i.e. immigrants), and the percent who were literate. He found the correlation between the 48 pairs of numbers was .53. This ecological correlation suggested a positive association between foreign birth and literacy: the foreign-born (immigrants) are more likely to be literate than the native-born. In reality, the association was negative: the correlation computed at the individual level was −0.11 (immigrants were less literate than native citizens). The ecological correlation gave the incorrect inference. This is because the foreign-born (immigrants) tended to migrate to and settle in states where the native-born are relatively literate. In this example by Robinson, the correlation is totally reversed. <br />
</blockquote><br />
<br />
[http://ije.oxfordjournals.org/content/40/4/1123.full Robinson’s data] look this way:<br />
<br />
<center> [[File:Robinson.png | 450 px]] </center><br />
<br />
[http://blog.statwing.com/the-ecological-fallacy/ The following graph] dealing this time with income and being foreign born is even more striking:<br />
<br />
<br />
<center> [[File:Income.png | 450 px]] </center><br />
<blockquote><br />
U.S. states with proportionally more immigrants have proportionally more households with income above $100k. Ergo, immigrants are more likely than non-immigrants to have household incomes above $100k.<br />
<br><br><br />
Hopefully something feels off about that logic. Because it’s wrong. Actually the relationship between income and being an immigrant at the individual level is the opposite.<br />
<br><br><br />
<center> [[File:Foreign-Born-vs-Income-Indiv.png | 250 px]] </center><br />
Deducing from the first chart that immigrants are more likely to be well-off is committing the ecological fallacy—attributing qualities at the individual level because of a relationship at a group level.<br />
</blockquote><br />
But here is a more recent and more difficult-to-unravel ecological fallacy:<br />
<br />
<blockquote><br />
That example was pretty easy to catch, not least because it feels intuitive that immigrants would tend to have lower income than non-immigrants. <br />
<br><br><br />
But not all ecological fallacies are so easy to spot.<br />
For example, there’s a negative correlation between per capita income in a state and the percent of the 2012 presidential election vote that went to Romney.<br />
<center> [[File: Income-vs-Republican.png| 450 px]] </center><br />
<br><br><br />
It’s easy to picture rich and liberal cities like San Francisco and New York, hear the phrase “latte liberal” a couple times, and believe that higher income is in fact correlated with voting Democratic.<br />
At an individual level, though, higher income is associated with voting Republican.<br />
<center> [[File: Republican-Vote-Share.jpg | 350 px]] </center><br />
The (simplified) explanation for this apparent paradox? Across the country, lower income folk tend to vote Democrat; within blue states, upper income folk also vote Democrat, but in red states they vote Republican. <br />
</blockquote><br />
<br />
A general way to look at where the fallacy might arise is via the [http://www.teachepi.org/documents/courses/bfiles/The%20B%20Files_File3_Durkheim_Final_Complete.pdf following graph and explanation of Durkheim’s suicide data]:<br />
<br />
<center>[[File:Dirkheim_expl.png | 600px]]</center><br />
<br />
That is, within every group it is possible that even if the correlation (regression line) is negative, it can happen that across the groups, the correlation (regression line) is positive. Note too that in many situations the “within” is not a cloud of points, each of which represents an individual, but instead, there is just one point, average exposure and average outcome. Further, exposure may come from one data base and outcome from another data base. This is totally unlike the kidney stones example which began this wiki because there stone and success can be tied to a particular individual. <br />
<br />
===Discussion===<br />
<br />
1. An oft-used synonym for the ecological fallacy (inferring from group to individuals) is called cross level inference. The opposite of the ecological fallacy is the atomistic fallacy (inferring from the individuals to the group).<br />
<br />
2. With regard to Robinson’s data, besides the fallacy aspect, what is wrong with doing a correlation in the first place?<br />
<br />
3. Concerning the graph of foreign born and income, suppose the ordinates were interchanged. How is this then similar to Durkheim’s study and its ecological fallacy?<br />
<br />
4. [http://en.wikipedia.org/wiki/Low_birth_weight_paradox The paradox of the smoking mother] is supposedly explained by the following:<br />
<blockquote><br />
The birth weight distribution for children of smoking mothers is shifted to lower weights by their mothers' actions. Therefore, otherwise healthy babies (who would weigh more if it were not for the fact their mother smoked) are born underweight. They have a lower mortality rate than children who have other medical reasons why they are born underweight, regardless of the fact their mother does not smoke.<br />
In short, smoking may be harmful in that it contributes to low birth weight, but other causes of low birth weight are generally more harmful only with regard to their weight.<br />
</blockquote><br />
How does this explanation accord with the aforementioned phrase, situation dependent?<br />
<br />
Submitted by Paul Alper<br />
<br />
==Normal vs. paranormal==<br />
John Allen Paulos sent a link to the following cartoon, reproduced below as presented on the StackExchange blog [http://stats.stackexchange.com/posts/14356/revisions Cross Validated]:<br />
<center>[[File:T2XrE.gif]]<br />
<br>From: '''A visual comparison of normal and paranormal distributions'''<br> Matthew Freeman ''J Epidemiol Community Health'' 2006;60:6. <br>Lower caption says 'Paranormal Distribution'- no idea why the graphical artifact is occuring.<br />
</center><br />
<br />
==Gallup reviewing its methods==<br />
[http://www.huffingtonpost.com/2013/03/08/gallup-presidential-poll_n_2806361.html “Gallup Presidential Poll: How Did Brand-Name Firm Blow Election?”]<br><br />
<i>HuffPost Pollster</i>, March 8, 2013<br><br />
<br />
The article discusses Gallup’s consistently favorable-to-Romney poll results over the Fall 2012 presidential election cycle, including a final Romney 49%-Obama 48% result. (Of course, 49 to 48 does not a winning prediction make.) It includes a nice scatterplot illustrating that the Gallup results deviated remarkably (not necessarily “significantly”) from other national polls over this period.<br><br />
<br />
Apparently Gallup revised its methodology re presidential approval polling in October 2012, in order to correct an “under-representation of non-whites in its samples.” Another nice scatterplot shows how Gallup’s rating results moved more into line with other polls’ results in October of the period July 2012-January 2013.<br><br />
<br />
The article also contains a somewhat detailed discussion of two serious problems facing Gallup and other pollsters today: “how they treat their ‘likely voter’ models and how they draw their samples from the general population.” These are issues associated with identifying likely voters and with reaching them by phone.<br><br />
<br />
Submitted by Margaret Cibes<br />
<br />
==Naked Statistics== <br />
<br />
Charles Wheelan’s book, ''Naked Statistics: Stripping the Dread from the Data'', is a breezy fun-filled read, his “homage to an earlier W.W. Norton classic, ''How to Lie with Statistics'' by Daryll Huff. Without my wishing to imply anything negative, a few decades back, ''Naked Statistics'' would be an ideal text for a course entitled, “Statistics for Poets.” Today, even poets, drama students, and people whose specialty is 17th century French drama (perhaps unfortunately) really need to learn some basic statistics. From the very Introduction to the book, he emphasizes his distaste for mathematics for mathematics sake: “What is the area beneath a parabola? Who Cares?” Yet, he likes physics which uses the same math “Because physics has a clear purpose.” Likewise, “I love statistics,” a comment not often seen or heard outside of Chance News. <br />
<br />
As he puts it<br />
<blockquote><br />
The paradox of statistics is that they are everywhere--from batting averages to presidential polls--but the discipline itself has a reputation for being uninteresting and inaccessible. Many statistics books and classes are overly laden with math and jargon. Believe me, the technical details are crucial (and interesting)--but it’s just Greek if you don’t understand the intuition. And you may not even care about the intuition if you’re not convinced that there is any reason to learn it. Every chapter in this book promises to answer the basic question that I asked (to no effect) of my high school calculus teacher: What is the point of this?<br />
<br><br><br />
The point is that statistics helps process data, which is really just a fancy name for information.<br />
</blockquote><br />
<br />
His motto is “Statistics can be really interesting, and most of it isn’t that difficult.” By the end of the book the reader is confronting regression analysis, which he calls “the miracle elixir, and in the next chapter, why it may not be. His examples vary from the amusingly bizarre to the downright practical. ''Naked Statistics'' is an ideal gift to a significant other who loves you but wonders about what you actually do with your time.<br />
<br />
'''Discussion'''<br />
<br />
1. On page xii he reveals “a career epiphany” he had at math camp. The math teacher was describing without any physical context that the infinite (geometric) series <br />
1+1/2 + 1/4 + 1/8 +…converges to a finite number. Wheelan came up with the following context to make it meaningful to him: A wall is two feet away and your first move is one foot, followed by a move of 1/2 foot, followed by a move of 1/4 foot and so on until you are “pretty darn close to the wall.” What would happen to you and the wall if the infinite series was instead 1+1/2 + 1/3 +1/4 + 1/5 +1/6 + 1/7 +1/8 +…?<br />
<br />
2. Nate Silver’s book, ''The Signal and the Noise'', is a hymn to Bayesian statistics. ‘’Naked Statistics’’ has no mention whatever of Bayes or Silver so that your significant other will have to do some outside reading. Wheelan promises that his second edition will include Bayesian concepts.<br />
<br />
3. [http://www.nytimes.com/2013/01/29/science/naked-statistics-by-charles-wheelan-review.html The review in the ''NYT''] put it this way: <br />
<br />
<blockquote><br />
While a great measure of the book’s appeal comes from Mr. Wheelan’s fluent style — a natural comedian, he is truly the Dave Barry of the coin toss set — the rest comes from his multiple real world examples illustrating exactly why even the most reluctant mathophobe is well advised to achieve a personal understanding of the statistical underpinnings of life, whether that individual is watching football on the couch, picking a school for the children or jiggling anxiously in a hospital admitting office.<br><br><br />
Are you a fan of those handy ranking systems based on performance data, guaranteed to steer you to the best surgeons in town? If so, you are up to your armpits in descriptive statistics, and Mr. Wheelan has some advice for you: beware. The easiest way for doctors to game those numbers is by avoiding the sickest patients.<br />
</blockquote><br />
<br />
How do college football and basketball teams similarly game the numbers?<br />
<br />
4. At the same ''NYT'' review there is an accompanying graphic taken from Wheelan’s book:<br />
<center>[[File:NYT_29scibooks-graphic-popup.jpg | 400px]]</center><br />
From the graphic, why would a (Pearson product-moment) correlation be misleading? Why the “reverse causality”?<br />
<br />
[Note: The ''NYT'' also provided [http://graphics8.nytimes.com/packages/pdf/science/naked-stats-excerpt.pdf this excerpt] from the book's introductory chapter.]<br />
<br />
Submitted by Paul Alper<br />
<br />
==Miscellaneous stats news==<br />
From <i>The Wall Street Journal</i>:<br><br />
<br />
"One [issue] is, if we see a sequence of words, how can we best guess which word is likely to come next. …. The other is how does that relate to the way a user actually interacts with their [sic] touch screen. The way we do this is essentially by modeling the surface of the keyboard as a series of probability distributions. What that means in layman’s terms is, the keyboard looks a bit like a mountain range with a peak where the user perceives each of the keys to be. We collect the points that you touch the screen, and we form and mold the mountains around those points. That gives us a unique snapshot of the way you perceive your keyboard. If we solve that problem, that gives us probabilities we can also use with the language probabilities we have, and then we tie these things together. What comes out at the end is the solution to this central mathematical problem — how do I guess what the user is trying to say.”<br />
<div align=right>Tech officer for Android in [http://blogs.wsj.com/digits/2013/03/18/the-science-behind-guessing-what-youll-type-next/?KEYWORDS=from+a+molehill+to+a+mountain+-+graphic “The Science Behind What You’ll Type Next”], March 18, 2013</div><br />
<br />
“The training of data scientists hasn't caught up with that demand, leaving companies searching for talent and especially, some say, for the relatively few people with extensive experience in the field. …. Tech workers with a full complement of big-data analysis skills are paid on average 11.5% more than people without those skills ….”<br />
<div align=right>[http://online.wsj.com/article/SB10001424127887323549204578319930378618430.html?KEYWORDS=spencer+e+ante “Help Wanted! Data, data everywhere – and not enough people to decipher it”], March 8, 2013</div><br />
<br />
“Poring once more over a 12-year-old set of data on breast-cancer tumors, Dr. Lum saw correlations between the disease and patients' outcomes that she and her fellow researchers had never noticed before …. Dr. Lum's new view came courtesy of software that uses topology, a branch of math that compresses relationships in complex data into shapes researchers can manipulate and probe: in this case, a Y, like a two-eared worm. …. [R]esearchers increasingly are scouring scientific papers and esoteric branches of mathematics like topology to make sense of complex data sets. …. Using graph theory, a tool similar to topology, IBM is mapping interactions of people on social networks, including its own.”<br />
<div align=right>[http://online.wsj.com/article/SB10001424127887323452204578288264046780392.html?KEYWORDS=deborah+gage “The New Shape of Big Data”], March 8, 2013</div><br />
<br />
Also so [http://online.wsj.com/article/SB10001424127887324196204578298381588348290.html?KEYWORDS=shira+ovide “Big Data, Big Blunders”], March 8, 2013<br><br />
<br />
Submitted by Margaret Cibes<br />
==Mediterranean diet==<br />
[http://www.nytimes.com/2013/02/26/health/mediterranean-diet-can-cut-heart-disease-study-finds.html?pagewanted=all&_r=1& Mediterranean diet shown to ward off heart attack and stroke]<br><br />
by Gina Kolata, ''New York Times'', 25 February 2013<br />
<br />
Diets come and diets go: high protein, Atkins, South Beach, Dash, Weight Watchers, low carb, no carb. But then there is the perennial favorite, the so-called Mediterranean diet which has generated some recent positive publicity. According to the NYT article:<br />
<blockquote><br />
The findings, published on The New England Journal of Medicine’s Web site on Monday, were based on the first major clinical trial to measure the diet’s effect on heart risks. The magnitude of the diet’s benefits startled experts. The study ended early, after almost five years, because the results were so clear it was considered unethical to continue.<br />
</blockquote><br />
According to someone who was not connected with this study conducted from Spain,<br />
<blockquote><br />
“And the really important thing — the coolest thing — is that they used very meaningful endpoints. They did not look at risk factors like cholesterol or hypertension or weight. They looked at heart attacks and strokes and death. At the end of the day, that is what really matters.”<br />
</blockquote><br />
<br />
This randomized, open-label clinical trial “assigned 7,447 people in Spain who were overweight, were smokers, or had diabetes or other risk factors for heart disease to follow the Mediterranean diet or a low-fat one.” The low-fat diet was the control and the Mediterranean diet had two arms, one with nuts and the other with extra-virgin olive oil.<br />
<br />
Reproduced below is a graph from the NYT article that highlights the benefits of either form of the Mediterranean diet.<br />
<br />
::[[File:NYT_HeartDiseaseAndDiet.gif]]<br />
<br />
The claim is that “about 30 percent of heart attacks, strokes and deaths from heart disease can be prevented in people at high risk if they switch to a Mediterranean diet.”<br />
<br />
===Discussion===<br />
<br />
1. The NEJM study itself may be found [http://www.nejm.org/doi/full/10.1056/NEJMoa1200303?query=featured_home#t=abstract here]. Its results are stated thusly:<br />
<blockquote><br />
RESULTS<br />
A total of 7447 persons were enrolled (age range, 55 to 80 years); 57% were women. The two Mediterranean-diet groups had good adherence to the intervention, according to self-reported intake and biomarker analyses. A primary end-point event occurred in 288 participants. The multivariable-adjusted hazard ratios were 0.70 (95% confidence interval [CI], 0.54 to 0.92) and 0.72 (95% CI, 0.54 to 0.96) for the group assigned to a Mediterranean diet with extra-virgin olive oil (96 events) and the group assigned to a Mediterranean diet with nuts (83 events), respectively, versus the control group (109 events). No diet-related adverse effects were reported.<br />
</blockquote><br />
2. Here are the explicit recommendations for the Mediterranean diet and the (control) low-fat diet:<br />
<blockquote><br />
'''Mediterranean diet''' <br><br />
''Recommended''<br><br />
:Olive oil(*) ≥4 tbsp/day<br><br />
:Tree nuts and peanuts† ≥3 servings/wk<br><br />
:Fresh fruits ≥3 servings/day<br><br />
:Vegetables ≥2 servings/day<br><br />
:Fish (especially fatty fish), seafood ≥3 servings/wk<br><br />
:Legumes ≥3 servings/wk<br><br />
:Sofrito‡ ≥2 servings/wk<br><br />
:White meat Instead of red meat<br><br />
:Wine with meals (optionally, only for habitual drinkers) ≥7 glasses/wk.<br><br />
''Discouraged''<br><br />
:Soda drinks <1 drink/day<br><br />
:Commercial bakery goods, sweets, and pastries§ <3 servings/wk<br><br />
:Spread fats <1 serving/day<br><br />
:Red and processed meats <1 serving/day<br />
<br><br />
'''Low-fat diet''' (control)<br><br />
''Recommended''<br><br />
:Low-fat dairy products ≥3 servings/day<br><br />
:Bread, potatoes, pasta, rice ≥3 servings/day<br><br />
:Fresh fruits ≥3 servings/day<br><br />
:Vegetables ≥2 servings/wk<br><br />
:Lean fish and seafood ≥3 servings/wk<br><br />
''Discouraged''<br><br />
:Vegetable oils (including olive oil) ≤2 tbsp/day<br><br />
:Commercial bakery goods, sweets, and pastries§ ≤1 serving/wk<br><br />
:Nuts and fried snacks ≤1 serving /wk<br><br />
:Red and processed fatty meats ≤1 serving/wk<br><br />
:Visible fat in meats and soups¶ Always remove<br><br />
:Fatty fish, seafood canned in oil ≤1 serving/wk<br><br />
:Spread fats ≤1 serving/wk<br><br />
:Sofrito‡ ≤2 servings/wk<br />
<br />
(*)The amount of olive oil includes oil used for cooking and salads and oil consumed in meals eaten outside the home. In the group assigned to the Mediterranean diet with extra-virgin olive oil, the goal was to consume 50 g (approximately 4 tbsp) or more per day of the polyphenol-rich olive oil supplied, instead of the ordinary refined variety, which is low in polyphenols.<br><br />
†For participants assigned to the Mediterranean diet with nuts, the recommended consumption was one daily serving (30 g, composed of 15 g of walnuts, 7.5 g of almonds, and 7.5 g of hazelnuts).<br><br />
‡Sofrito is a sauce made with tomato and onion, often including garlic and aromatic herbs, and slowly simmered with olive oil.<br><br />
§ Commercial bakery goods, sweets, and pastries (not homemade) included cakes, cookies, biscuits, and custard.<br><br />
¶Participants were advised to remove the visible fat (or the skin) of chicken, duck, pork, lamb, or veal before cooking and the fat of soups, broths, and cooked meat dishes before consumption. <br />
</blockquote><br />
<br />
3. Why would the above recommendations be difficult to follow in some parts of the world? Google sofrito to see if you have consumed it under another name. Comment on the inexactness of the term “a serving.”<br />
<br />
4. “Peanuts” are part of the recommendations but in the footnote, only walnuts, almonds and hazel nuts appear. Try to come up with an explanation for the exclusion of peanuts.<br />
<br />
5. The Mediterranean while not as large as the Atlantic or the Pacific, does include North Africa as well as many European countries. If your ancestors come from one of those places, comment on how your Mediterranean cuisine heritage might differ from the above recommendations when it comes to cheese, meat, wine, butter, etc.<br />
<br />
6. If strokes, heart attacks and death are more meaningful--as they definitely are--than surrogate criteria such as cholesterol, blood pressure and weight gain, why do so many studies look at surrogate measures only?<br />
<br />
7. This study had 18 authors some of whom served on the board of the Research Foundation on Wine and Nutrition, received support from the California Walnut Commision, the International Nut and Dried Food Council, Nestle, PepsiCo, the Beer and Health Foundation and Danone.<br />
<br />
8. The authors state that “Among persons at high cardiovascular risk, a Mediterranean diet supplemented with extra-virgin olive oil or nuts reduced the incidence of major cardiovascular events.” Why was no conclusion drawn regarding persons who are at low cardiovascular risk?<br />
<br />
9. But maybe the last word on the subject of diets can be found in the NYT article. Dr. Esselstyn, a noted vegan, remarked<br />
<blockquote><br />
those in the Mediterranean diet study still had heart attacks and strokes. So, he said, all the study showed was that “the Mediterranean diet and the horrible control diet were able to create disease in people who otherwise did not have it.”<br />
</blockquote><br />
<br />
10. Let us not forget the famous phrase popular among our forbearers: when it comes to diets, there are really only two: food and no food.<br />
<br />
Submitted by Paul Alper<br />
<br />
==Using genetic data without permission==<br />
<br />
[http://www.nytimes.com/2013/03/24/opinion/sunday/the-immortal-life-of-henrietta-lacks-the-sequel.html The Immortal Life of Henrietta Lacks, the Sequel], Rebecca Skloot, The New York Times, March 23, 2013.<br />
<br />
<p>There is a pompous attitude that some people take when they are told about research abuses. It goes along the lines of "That happened years ago, and with all of today's safeguards, it could never happen again." Well, maybe, but this article provides a healthy reminder that sometimes we don't learn from our mistakes.</p><br />
<br />
<p>Rebecca Skloot wrote an excellent book (The Immortal Life of Henrietta Lacks) about Henrietta Lacks and a line of cells (HeLa) derived from a tumor that killed her in 1951. This book is worth reading if you are involved with research because it deals with the issue of taking tissues from a person and using them for research with getting consent first. It also talks about abuses of Henrietta Lacks's family. Most of the problems occured before we had the Belmont Report and Institutional Review Boards. But recently, researchers, presumably all trained in the proper conduct of research, made a very similar mistake with the same family. Scientists sequenced and published the full genome from the HeLa cell line. They did this without seeking the consent of family members</p><br />
<br />
<p>Research on a dead person normally has few barriers and this is often reasonable. But genetic information is different because it reveals something more. Genetic information provides data not just on the dead person but on any living relatives. This is not universally appreciated, even by experts in the field.</p><br />
<br />
<blockquote>A news release from the European Molecular Biology Laboratory, where the HeLa genome was sequenced, said, "We cannot infer anything about Henrietta Lacks’s genome, or of her descendants, from the data generated in this study."</blockquote><br />
<br />
But this data can be combined with publicly available resources to surprising results. One scientist<br />
<br />
<blockquote>uploaded HeLa’s genome to a public Web site called SNPedia, a Wikipedia-like site for translating genetic information. Minutes later, it produced a report full of personal information about Henrietta Lacks, and her family. (The scientist kept that report confidential, sharing it only with me.) Until recently, few people had the ability to process raw genome data like this. Now anyone who can send an e-mail can do it. No one knows what we may someday learn about Lacks’s great-grandchildren from her genome, but we know this: the view we have today of genomes is like a world map, but Google Street View is coming very soon. </blockquote><br />
<br />
<p>There is a growing awareness in the research community that DNA sequences, in particular, raise difficult issues that are unaddressed by current regulations.</p><br />
<br />
<blockquote>The problem, says Yaniv Erlich, a fellow at the Whitehead Institute for Biomedical Research, is that anonymity vanishes when it comes to DNA: “People don’t realize it’s impossible to hide genetic information once it’s out there.” He and his colleagues recently proved that it’s possible to use online public databases to find the identities of people whose anonymous DNA samples had been sequenced and published online. Yet researchers aren’t required to tell you that there is no guarantee that a genome, once sequenced, will stay private or anonymous. </blockquote><br />
<br />
===Discussion===<br />
<br />
1. What are some of the problems that can occur when genetic information about an individual becomes publicly available?<br />
<br />
2. Would you consider letting your DNA sequence be openly published for the benefit of research? Would you check first with your parents, siblings, or children before doing this?<br />
<br />
3. Can reasonable safeguards be put in place to allow distribution of gene sequence data without compromising privacy?</div>Simon66217https://www.causeweb.org/wiki/chance/index.php?title=Chance_News_92&diff=17207Chance News 922013-03-27T19:31:33Z<p>Simon66217: /* Mediterranean diet */</p>
<hr />
<div>==Quotations==<br />
"I've done the calculation and your chances of winning the lottery are identical whether you play or not."<br />
<div align=right>--Fran Lebowitz (American author and humorist)</div><br />
<br />
Suggested by Naomi Neff (with thanks to Cynthia Slater)<br />
<br />
----<br />
"As much as it pleases me to see statistical data introduced in the Supreme Court, the act of citing statistical factoids is not the same thing as drawing sound inferences from them."<br />
<br />
<div align=right>--Nate Silver, [http://fivethirtyeight.blogs.nytimes.com/2013/03/07/in-supreme-court-debate-on-voting-rights-act-a-dubious-use-of-statistics/ In Supreme Court Debate on Voting Rights Act, a Dubious Use of Statistics] FiveThirtyEight blog</div><br />
<br />
Submitted by Paul Alper<br />
<br />
----<br />
“economisting … 1. The act or process of converting limited evidence into grand claims by means of punning, multiplicity of meaning, and over-reaching. 2. The belief or practice that empirical evidence can only confirm and never disconfirm a favored theory. 3. Conclusions that are theory-driven, not evidence-based.”<br />
<div align=right>Anthropologist Clifford Geertz, <i>Available Light: Anthropological Reflections on Philosophical Topics</i>, Princeton, 2000<br><br />
quoted by Edward Tufte in his [http://www.edwardtufte.com/bboard/q-and-a-fetch-msg?msg_id=0001Zl <i>Beautiful Evidence</i>], Graphics Press, 2006</div><br />
Submitted by Margaret Cibes<br />
<br />
----<br />
"Statistics books almost always illustrate this point by drawing colored marbles out of an urn. (In fact, it's about the only place where one sees the word 'urn' used with any regularity.)<br />
<br />
<div align=right>--Charles Wheelan, ''Naked Statistics'' (p. 112)</div><br />
<br />
Submitted by Paul Alper<br />
<br />
==Forsooth==<br />
Miracles of the loaves and fishes, from <i>The Wall Street Journal</i> ....<br><br />
<br />
“The quants have arrived at the Academy [of Motion Picture Arts and Sciences]. …. The goals in making ... predictions extend beyond [Oscar night]. Dr. Rothschild [Microsoft Research economist] is testing whether surveying people online about Oscar patterns—for example, does winning best-adapted screenplay correspond with winning best picture?—is a method that can be translated to forecasting in other areas. If it works, ‘We can apply it to all sorts of other things we don't have data for,’ Dr. Rothschild said."<br />
<div align=right>Carl Bialik in [http://online.wsj.com/article/SB10001424127887324503204578318682787064790.html?KEYWORDS=carl+bialik#articleTabs%3Darticle “And the Oscar-Pool Winners Are...the Stats Dudes”]<br><br />
by Carl Bialik, February 23, 2013</div><br />
<br />
<center>[[file:Extrapolation.jpg|150px]]</center><br />
<div align=right>[http://online.wsj.com/article/SB10001424127887324077704578358231107272180.html?mg=id-wsj “Big Data Broadens Its Range”], March 13, 2013</div><br />
Submitted by Margaret Cibes<br />
<br />
==Simpson’s paradox and the ecological fallacy==<br />
<br />
The lay public tends to believe that statistics is merely a (rather dull) branch of mathematics. In fact, the discipline of statistics should be viewed as a science, as exemplified by physics, astronomy, chemistry, etc., which uses mathematics extensively and is situation dependent. In other word, the same numbers lead to different conclusion depending on the context.<br />
<br />
Prime examples of situation dependency may be found in the discussions of Simpson’s paradox and the even more subtle phenomenon known as the ecological fallacy. A treatment of the former can sometimes be found in elementary statistics textbooks but the latter, being less intuitive, is relatively rare in textbooks but often pops up in learned discussions where the reader is warned about drawing false conclusions.<br />
<br />
The dating of the phenomenon now known as Simpson’s paradox goes back before any of the current Chance News readers were born; the bestowing of the name, according to [http://en.wikipedia.org/wiki/Simpson's_paradox Wikipedia], originated much later in 1971:<br />
<blockquote><br />
Simpson's paradox (or the Yule–Simpson effect) is a paradox in which a trend [i.e., inequality] that appears in different groups of data disappears when these groups are combined, and the reverse trend [i.e., opposite inequality] appears for the aggregate data. This result is often encountered in social-science and medical-science statistics, and is particularly confounding when frequency data are unduly given causal interpretations.<br />
</blockquote><br />
The Wikipedia article has this “real-life example from a medical study comparing the success rates of two treatments for kidney stones.”<br />
<br />
<table class="wikitable" summary="results accounting for stone size" style="margin-left:auto; margin-right:auto;"><br />
<tr><br />
<th></th><br />
<th>Treatment A</th><br />
<th>Treatment B</th><br />
</tr><br />
<tr align="center"><br />
<th>Small Stones</th><br />
<td><i>Group 1</i><br /><br />
<b>93% (81/87)</b></td><br />
<td><i>Group 2</i><br /><br />
87% (234/270)</td><br />
</tr><br />
<tr align="center"><br />
<th>Large Stones</th><br />
<td><i>Group 3</i><br /><br />
<b>73% (192/263)</b></td><br />
<td><i>Group 4</i><br /><br />
69% (55/80)</td><br />
</tr><br />
<tr align="center"><br />
<th>Both</th><br />
<td>78% (273/350)</td><br />
<td><b>83% (289/350)</b></td><br />
</tr><br />
</table><br />
<blockquote><br />
The paradoxical conclusion is that treatment A is more effective when used on small stones, [93% > 87%] and also when used on large stones, [73% > 69%] yet treatment B is more effective when considering both sizes at the same time [78% < 83%]. In this example, the "lurking" variable (or confounding variable) of the stone size was not previously known to be important until its effects were included.<br />
</blockquote><br />
In this context of kidney stones, it is clear that disaggregation makes sense and Treatment A is preferable to Treatment B despite Treatment B being better in the aggregate sense. However, if we take the same numbers but change the context to Athletic Team A and Athletic Team B who play Small and Large opponents and the only thing that determines ranking is the total winning percentage, then Athletic Team B is preferred to Athletic Team A. That is, aggregation makes sense in this scenario as it did not in the original Wikipedia presentation. <br />
<br />
Other interesting examples are provided in the Wikipedia article. When money is at stake, as in the “Berkeley gender bias” case discussed in Wikipedia, finding a lurking (confounding) variable requires some clever slicing to find “Small” and “Large” which will reverse the inequality. The Wikipedia article also refers to the so-called “low birth rate paradox” whereby “it has been observed that babies of low birth weights born to smoking mothers have a lower mortality rate than the babies of low birth weights of non-smokers.” The paradoxical implication is that smoking helps to lower mortality of newborns. More on this below.<br />
<br />
[http://en.wikipedia.org/wiki/Ecological_fallacy A different Wikipedia article] has two definitions of the ecological fallacy. The first definition focuses on aggregation and disaggregation. With this definition, Simpson’s paradox is subsumed under the ecological fallacy:<br />
<blockquote><br />
An ecological fallacy (or ecological inference fallacy) is a logical fallacy in the interpretation of statistical data where inferences about the nature of individuals [disaggregation] are deduced from inference for the group [aggregation] to which those individuals belong. <br />
</blockquote><br />
The second definition spotlights the notion of correlation:<br />
<blockquote><br />
<br />
Ecological fallacy can refer to the following statistical fallacy: the correlation between individual variables is deduced from the correlation of the variables collected for the group to which those individuals belong.<br />
</blockquote><br />
<br />
Although elementary statistics textbooks do not customarily mention the ecological fallacy, it is even older than Simpson’s paradox. The term was first coined in 1950 by William Robinson but goes back to Emile Durkheim’s 1897 study of suicide. [http://www.teachepi.org/documents/courses/bfiles/The%20B%20Files_File3_Durkheim_Final_Complete.pdf From the graph], it appears that the greater the proportion of Protestants, the greater is the suicide rate:<br />
<br />
<center>[[File:Suicide.png | 600px]]</center><br />
<br />
<blockquote><br />
According to Morgenstern, the estimated rate ratio of 7.6 was probably not because suicide rates were nearly 8 fold higher in Protestants than in non-Protestants. Rather, because none of the regions was entirely Protestant or non-Protestant, it may have been non-Protestants (primarily Catholics) who were committing suicide in predominantly Protestant provinces. It is plausible that members of a religious minority might have been more likely to commit suicide than were members of the majority. Living in a <br />
predominantly Protestant area had a contextual effect on suicide risk among Catholics.<br />
<br><br><br />
Interestingly, Morgenstern points out that Durkheim compared the suicide rates at the individual level for Protestants, Catholics and Jews living in Prussia, and from his data, the rate was about twice as great in Protestants as in other religious groups. Thus, when the rate ratios are compared (2 vs 8), there appears to be substantial ecological bias using the aggregate level data.<br />
</blockquote><br />
<br />
In the above situation there was no reversal of an inequality, merely a sharp diminishing from aggregated to disaggregated. The following example of the ecological fallacy actually illustrates the reversal. <br />
<blockquote><br />
<br />
One compelling example by Robinson (1950), was the relationship between nativity (foreign vs native born) and literacy. For each of the 48 states in the USA of 1930, [there were only 48 states admitted to the Union by 1930] Robinson computed two numbers: the percent of the population who were foreign-born (i.e. immigrants), and the percent who were literate. He found the correlation between the 48 pairs of numbers was .53. This ecological correlation suggested a positive association between foreign birth and literacy: the foreign-born (immigrants) are more likely to be literate than the native-born. In reality, the association was negative: the correlation computed at the individual level was −0.11 (immigrants were less literate than native citizens). The ecological correlation gave the incorrect inference. This is because the foreign-born (immigrants) tended to migrate to and settle in states where the native-born are relatively literate. In this example by Robinson, the correlation is totally reversed. <br />
</blockquote><br />
<br />
[http://ije.oxfordjournals.org/content/40/4/1123.full Robinson’s data] look this way:<br />
<br />
<center> [[File:Robinson.png | 450 px]] </center><br />
<br />
[http://blog.statwing.com/the-ecological-fallacy/ The following graph] dealing this time with income and being foreign born is even more striking:<br />
<br />
<br />
<center> [[File:Income.png | 450 px]] </center><br />
<blockquote><br />
U.S. states with proportionally more immigrants have proportionally more households with income above $100k. Ergo, immigrants are more likely than non-immigrants to have household incomes above $100k.<br />
<br><br><br />
Hopefully something feels off about that logic. Because it’s wrong. Actually the relationship between income and being an immigrant at the individual level is the opposite.<br />
<br><br><br />
<center> [[File:Foreign-Born-vs-Income-Indiv.png | 250 px]] </center><br />
Deducing from the first chart that immigrants are more likely to be well-off is committing the ecological fallacy—attributing qualities at the individual level because of a relationship at a group level.<br />
</blockquote><br />
But here is a more recent and more difficult-to-unravel ecological fallacy:<br />
<br />
<blockquote><br />
That example was pretty easy to catch, not least because it feels intuitive that immigrants would tend to have lower income than non-immigrants. <br />
<br><br><br />
But not all ecological fallacies are so easy to spot.<br />
For example, there’s a negative correlation between per capita income in a state and the percent of the 2012 presidential election vote that went to Romney.<br />
<center> [[File: Income-vs-Republican.png| 450 px]] </center><br />
<br><br><br />
It’s easy to picture rich and liberal cities like San Francisco and New York, hear the phrase “latte liberal” a couple times, and believe that higher income is in fact correlated with voting Democratic.<br />
At an individual level, though, higher income is associated with voting Republican.<br />
<center> [[File: Republican-Vote-Share.jpg | 350 px]] </center><br />
The (simplified) explanation for this apparent paradox? Across the country, lower income folk tend to vote Democrat; within blue states, upper income folk also vote Democrat, but in red states they vote Republican. <br />
</blockquote><br />
<br />
A general way to look at where the fallacy might arise is via the [http://www.teachepi.org/documents/courses/bfiles/The%20B%20Files_File3_Durkheim_Final_Complete.pdf following graph and explanation of Durkheim’s suicide data]:<br />
<br />
<center>[[File:Dirkheim_expl.png | 600px]]</center><br />
<br />
That is, within every group it is possible that even if the correlation (regression line) is negative, it can happen that across the groups, the correlation (regression line) is positive. Note too that in many situations the “within” is not a cloud of points, each of which represents an individual, but instead, there is just one point, average exposure and average outcome. Further, exposure may come from one data base and outcome from another data base. This is totally unlike the kidney stones example which began this wiki because there stone and success can be tied to a particular individual. <br />
<br />
===Discussion===<br />
<br />
1. An oft-used synonym for the ecological fallacy (inferring from group to individuals) is called cross level inference. The opposite of the ecological fallacy is the atomistic fallacy (inferring from the individuals to the group).<br />
<br />
2. With regard to Robinson’s data, besides the fallacy aspect, what is wrong with doing a correlation in the first place?<br />
<br />
3. Concerning the graph of foreign born and income, suppose the ordinates were interchanged. How is this then similar to Durkheim’s study and its ecological fallacy?<br />
<br />
4. [http://en.wikipedia.org/wiki/Low_birth_weight_paradox The paradox of the smoking mother] is supposedly explained by the following:<br />
<blockquote><br />
The birth weight distribution for children of smoking mothers is shifted to lower weights by their mothers' actions. Therefore, otherwise healthy babies (who would weigh more if it were not for the fact their mother smoked) are born underweight. They have a lower mortality rate than children who have other medical reasons why they are born underweight, regardless of the fact their mother does not smoke.<br />
In short, smoking may be harmful in that it contributes to low birth weight, but other causes of low birth weight are generally more harmful only with regard to their weight.<br />
</blockquote><br />
How does this explanation accord with the aforementioned phrase, situation dependent?<br />
<br />
Submitted by Paul Alper<br />
<br />
==Normal vs. paranormal==<br />
John Allen Paulos sent a link to the following cartoon, reproduced below as presented on the StackExchange blog [http://stats.stackexchange.com/posts/14356/revisions Cross Validated]:<br />
<center>[[File:T2XrE.gif]]<br />
<br>From: '''A visual comparison of normal and paranormal distributions'''<br> Matthew Freeman ''J Epidemiol Community Health'' 2006;60:6. <br>Lower caption says 'Paranormal Distribution'- no idea why the graphical artifact is occuring.<br />
</center><br />
<br />
==Gallup reviewing its methods==<br />
[http://www.huffingtonpost.com/2013/03/08/gallup-presidential-poll_n_2806361.html “Gallup Presidential Poll: How Did Brand-Name Firm Blow Election?”]<br><br />
<i>HuffPost Pollster</i>, March 8, 2013<br><br />
<br />
The article discusses Gallup’s consistently favorable-to-Romney poll results over the Fall 2012 presidential election cycle, including a final Romney 49%-Obama 48% result. (Of course, 49 to 48 does not a winning prediction make.) It includes a nice scatterplot illustrating that the Gallup results deviated remarkably (not necessarily “significantly”) from other national polls over this period.<br><br />
<br />
Apparently Gallup revised its methodology re presidential approval polling in October 2012, in order to correct an “under-representation of non-whites in its samples.” Another nice scatterplot shows how Gallup’s rating results moved more into line with other polls’ results in October of the period July 2012-January 2013.<br><br />
<br />
The article also contains a somewhat detailed discussion of two serious problems facing Gallup and other pollsters today: “how they treat their ‘likely voter’ models and how they draw their samples from the general population.” These are issues associated with identifying likely voters and with reaching them by phone.<br><br />
<br />
Submitted by Margaret Cibes<br />
<br />
==Naked Statistics== <br />
<br />
Charles Wheelan’s book, ''Naked Statistics: Stripping the Dread from the Data'', is a breezy fun-filled read, his “homage to an earlier W.W. Norton classic, ''How to Lie with Statistics'' by Daryll Huff. Without my wishing to imply anything negative, a few decades back, ''Naked Statistics'' would be an ideal text for a course entitled, “Statistics for Poets.” Today, even poets, drama students, and people whose specialty is 17th century French drama (perhaps unfortunately) really need to learn some basic statistics. From the very Introduction to the book, he emphasizes his distaste for mathematics for mathematics sake: “What is the area beneath a parabola? Who Cares?” Yet, he likes physics which uses the same math “Because physics has a clear purpose.” Likewise, “I love statistics,” a comment not often seen or heard outside of Chance News. <br />
<br />
As he puts it<br />
<blockquote><br />
The paradox of statistics is that they are everywhere--from batting averages to presidential polls--but the discipline itself has a reputation for being uninteresting and inaccessible. Many statistics books and classes are overly laden with math and jargon. Believe me, the technical details are crucial (and interesting)--but it’s just Greek if you don’t understand the intuition. And you may not even care about the intuition if you’re not convinced that there is any reason to learn it. Every chapter in this book promises to answer the basic question that I asked (to no effect) of my high school calculus teacher: What is the point of this?<br />
<br><br><br />
The point is that statistics helps process data, which is really just a fancy name for information.<br />
</blockquote><br />
<br />
His motto is “Statistics can be really interesting, and most of it isn’t that difficult.” By the end of the book the reader is confronting regression analysis, which he calls “the miracle elixir, and in the next chapter, why it may not be. His examples vary from the amusingly bizarre to the downright practical. ''Naked Statistics'' is an ideal gift to a significant other who loves you but wonders about what you actually do with your time.<br />
<br />
'''Discussion'''<br />
<br />
1. On page xii he reveals “a career epiphany” he had at math camp. The math teacher was describing without any physical context that the infinite (geometric) series <br />
1+1/2 + 1/4 + 1/8 +…converges to a finite number. Wheelan came up with the following context to make it meaningful to him: A wall is two feet away and your first move is one foot, followed by a move of 1/2 foot, followed by a move of 1/4 foot and so on until you are “pretty darn close to the wall.” What would happen to you and the wall if the infinite series was instead 1+1/2 + 1/3 +1/4 + 1/5 +1/6 + 1/7 +1/8 +…?<br />
<br />
2. Nate Silver’s book, ''The Signal and the Noise'', is a hymn to Bayesian statistics. ‘’Naked Statistics’’ has no mention whatever of Bayes or Silver so that your significant other will have to do some outside reading. Wheelan promises that his second edition will include Bayesian concepts.<br />
<br />
3. [http://www.nytimes.com/2013/01/29/science/naked-statistics-by-charles-wheelan-review.html The review in the ''NYT''] put it this way: <br />
<br />
<blockquote><br />
While a great measure of the book’s appeal comes from Mr. Wheelan’s fluent style — a natural comedian, he is truly the Dave Barry of the coin toss set — the rest comes from his multiple real world examples illustrating exactly why even the most reluctant mathophobe is well advised to achieve a personal understanding of the statistical underpinnings of life, whether that individual is watching football on the couch, picking a school for the children or jiggling anxiously in a hospital admitting office.<br><br><br />
Are you a fan of those handy ranking systems based on performance data, guaranteed to steer you to the best surgeons in town? If so, you are up to your armpits in descriptive statistics, and Mr. Wheelan has some advice for you: beware. The easiest way for doctors to game those numbers is by avoiding the sickest patients.<br />
</blockquote><br />
<br />
How do college football and basketball teams similarly game the numbers?<br />
<br />
4. At the same ''NYT'' review there is an accompanying graphic taken from Wheelan’s book:<br />
<center>[[File:NYT_29scibooks-graphic-popup.jpg | 400px]]</center><br />
From the graphic, why would a (Pearson product-moment) correlation be misleading? Why the “reverse causality”?<br />
<br />
[Note: The ''NYT'' also provided [http://graphics8.nytimes.com/packages/pdf/science/naked-stats-excerpt.pdf this excerpt] from the book's introductory chapter.]<br />
<br />
Submitted by Paul Alper<br />
<br />
==Miscellaneous stats news==<br />
From <i>The Wall Street Journal</i>:<br><br />
<br />
"One [issue] is, if we see a sequence of words, how can we best guess which word is likely to come next. …. The other is how does that relate to the way a user actually interacts with their [sic] touch screen. The way we do this is essentially by modeling the surface of the keyboard as a series of probability distributions. What that means in layman’s terms is, the keyboard looks a bit like a mountain range with a peak where the user perceives each of the keys to be. We collect the points that you touch the screen, and we form and mold the mountains around those points. That gives us a unique snapshot of the way you perceive your keyboard. If we solve that problem, that gives us probabilities we can also use with the language probabilities we have, and then we tie these things together. What comes out at the end is the solution to this central mathematical problem — how do I guess what the user is trying to say.”<br />
<div align=right>Tech officer for Android in [http://blogs.wsj.com/digits/2013/03/18/the-science-behind-guessing-what-youll-type-next/?KEYWORDS=from+a+molehill+to+a+mountain+-+graphic “The Science Behind What You’ll Type Next”], March 18, 2013</div><br />
<br />
“The training of data scientists hasn't caught up with that demand, leaving companies searching for talent and especially, some say, for the relatively few people with extensive experience in the field. …. Tech workers with a full complement of big-data analysis skills are paid on average 11.5% more than people without those skills ….”<br />
<div align=right>[http://online.wsj.com/article/SB10001424127887323549204578319930378618430.html?KEYWORDS=spencer+e+ante “Help Wanted! Data, data everywhere – and not enough people to decipher it”], March 8, 2013</div><br />
<br />
“Poring once more over a 12-year-old set of data on breast-cancer tumors, Dr. Lum saw correlations between the disease and patients' outcomes that she and her fellow researchers had never noticed before …. Dr. Lum's new view came courtesy of software that uses topology, a branch of math that compresses relationships in complex data into shapes researchers can manipulate and probe: in this case, a Y, like a two-eared worm. …. [R]esearchers increasingly are scouring scientific papers and esoteric branches of mathematics like topology to make sense of complex data sets. …. Using graph theory, a tool similar to topology, IBM is mapping interactions of people on social networks, including its own.”<br />
<div align=right>[http://online.wsj.com/article/SB10001424127887323452204578288264046780392.html?KEYWORDS=deborah+gage “The New Shape of Big Data”], March 8, 2013</div><br />
<br />
Also so [http://online.wsj.com/article/SB10001424127887324196204578298381588348290.html?KEYWORDS=shira+ovide “Big Data, Big Blunders”], March 8, 2013<br><br />
<br />
Submitted by Margaret Cibes<br />
==Mediterranean diet==<br />
[http://www.nytimes.com/2013/02/26/health/mediterranean-diet-can-cut-heart-disease-study-finds.html?pagewanted=all&_r=1& Mediterranean diet shown to ward off heart attack and stroke]<br><br />
by Gina Kolata, ''New York Times'', 25 February 2013<br />
<br />
Diets come and diets go: high protein, Atkins, South Beach, Dash, Weight Watchers, low carb, no carb. But then there is the perennial favorite, the so-called Mediterranean diet which has generated some recent positive publicity. According to the NYT article:<br />
<blockquote><br />
The findings, published on The New England Journal of Medicine’s Web site on Monday, were based on the first major clinical trial to measure the diet’s effect on heart risks. The magnitude of the diet’s benefits startled experts. The study ended early, after almost five years, because the results were so clear it was considered unethical to continue.<br />
</blockquote><br />
According to someone who was not connected with this study conducted from Spain,<br />
<blockquote><br />
“And the really important thing — the coolest thing — is that they used very meaningful endpoints. They did not look at risk factors like cholesterol or hypertension or weight. They looked at heart attacks and strokes and death. At the end of the day, that is what really matters.”<br />
</blockquote><br />
<br />
This randomized, open-label clinical trial “assigned 7,447 people in Spain who were overweight, were smokers, or had diabetes or other risk factors for heart disease to follow the Mediterranean diet or a low-fat one.” The low-fat diet was the control and the Mediterranean diet had two arms, one with nuts and the other with extra-virgin olive oil.<br />
<br />
Reproduced below is a graph from the NYT article that highlights the benefits of either form of the Mediterranean diet.<br />
<br />
::[[File:NYT_HeartDiseaseAndDiet.gif]]<br />
<br />
The claim is that “about 30 percent of heart attacks, strokes and deaths from heart disease can be prevented in people at high risk if they switch to a Mediterranean diet.”<br />
<br />
===Discussion===<br />
<br />
1. The NEJM study itself may be found [http://www.nejm.org/doi/full/10.1056/NEJMoa1200303?query=featured_home#t=abstract here]. Its results are stated thusly:<br />
<blockquote><br />
RESULTS<br />
A total of 7447 persons were enrolled (age range, 55 to 80 years); 57% were women. The two Mediterranean-diet groups had good adherence to the intervention, according to self-reported intake and biomarker analyses. A primary end-point event occurred in 288 participants. The multivariable-adjusted hazard ratios were 0.70 (95% confidence interval [CI], 0.54 to 0.92) and 0.72 (95% CI, 0.54 to 0.96) for the group assigned to a Mediterranean diet with extra-virgin olive oil (96 events) and the group assigned to a Mediterranean diet with nuts (83 events), respectively, versus the control group (109 events). No diet-related adverse effects were reported.<br />
</blockquote><br />
2. Here are the explicit recommendations for the Mediterranean diet and the (control) low-fat diet:<br />
<blockquote><br />
'''Mediterranean diet''' <br><br />
''Recommended''<br><br />
:Olive oil(*) ≥4 tbsp/day<br><br />
:Tree nuts and peanuts† ≥3 servings/wk<br><br />
:Fresh fruits ≥3 servings/day<br><br />
:Vegetables ≥2 servings/day<br><br />
:Fish (especially fatty fish), seafood ≥3 servings/wk<br><br />
:Legumes ≥3 servings/wk<br><br />
:Sofrito‡ ≥2 servings/wk<br><br />
:White meat Instead of red meat<br><br />
:Wine with meals (optionally, only for habitual drinkers) ≥7 glasses/wk.<br><br />
''Discouraged''<br><br />
:Soda drinks <1 drink/day<br><br />
:Commercial bakery goods, sweets, and pastries§ <3 servings/wk<br><br />
:Spread fats <1 serving/day<br><br />
:Red and processed meats <1 serving/day<br />
<br><br />
'''Low-fat diet''' (control)<br><br />
''Recommended''<br><br />
:Low-fat dairy products ≥3 servings/day<br><br />
:Bread, potatoes, pasta, rice ≥3 servings/day<br><br />
:Fresh fruits ≥3 servings/day<br><br />
:Vegetables ≥2 servings/wk<br><br />
:Lean fish and seafood ≥3 servings/wk<br><br />
''Discouraged''<br><br />
:Vegetable oils (including olive oil) ≤2 tbsp/day<br><br />
:Commercial bakery goods, sweets, and pastries§ ≤1 serving/wk<br><br />
:Nuts and fried snacks ≤1 serving /wk<br><br />
:Red and processed fatty meats ≤1 serving/wk<br><br />
:Visible fat in meats and soups¶ Always remove<br><br />
:Fatty fish, seafood canned in oil ≤1 serving/wk<br><br />
:Spread fats ≤1 serving/wk<br><br />
:Sofrito‡ ≤2 servings/wk<br />
<br />
(*)The amount of olive oil includes oil used for cooking and salads and oil consumed in meals eaten outside the home. In the group assigned to the Mediterranean diet with extra-virgin olive oil, the goal was to consume 50 g (approximately 4 tbsp) or more per day of the polyphenol-rich olive oil supplied, instead of the ordinary refined variety, which is low in polyphenols.<br><br />
†For participants assigned to the Mediterranean diet with nuts, the recommended consumption was one daily serving (30 g, composed of 15 g of walnuts, 7.5 g of almonds, and 7.5 g of hazelnuts).<br><br />
‡Sofrito is a sauce made with tomato and onion, often including garlic and aromatic herbs, and slowly simmered with olive oil.<br><br />
§ Commercial bakery goods, sweets, and pastries (not homemade) included cakes, cookies, biscuits, and custard.<br><br />
¶Participants were advised to remove the visible fat (or the skin) of chicken, duck, pork, lamb, or veal before cooking and the fat of soups, broths, and cooked meat dishes before consumption. <br />
</blockquote><br />
<br />
3. Why would the above recommendations be difficult to follow in some parts of the world? Google sofrito to see if you have consumed it under another name. Comment on the inexactness of the term “a serving.”<br />
<br />
4. “Peanuts” are part of the recommendations but in the footnote, only walnuts, almonds and hazel nuts appear. Try to come up with an explanation for the exclusion of peanuts.<br />
<br />
5. The Mediterranean while not as large as the Atlantic or the Pacific, does include North Africa as well as many European countries. If your ancestors come from one of those places, comment on how your Mediterranean cuisine heritage might differ from the above recommendations when it comes to cheese, meat, wine, butter, etc.<br />
<br />
6. If strokes, heart attacks and death are more meaningful--as they definitely are--than surrogate criteria such as cholesterol, blood pressure and weight gain, why do so many studies look at surrogate measures only?<br />
<br />
7. This study had 18 authors some of whom served on the board of the Research Foundation on Wine and Nutrition, received support from the California Walnut Commision, the International Nut and Dried Food Council, Nestle, PepsiCo, the Beer and Health Foundation and Danone.<br />
<br />
8. The authors state that “Among persons at high cardiovascular risk, a Mediterranean diet supplemented with extra-virgin olive oil or nuts reduced the incidence of major cardiovascular events.” Why was no conclusion drawn regarding persons who are at low cardiovascular risk?<br />
<br />
9. But maybe the last word on the subject of diets can be found in the NYT article. Dr. Esselstyn, a noted vegan, remarked<br />
<blockquote><br />
those in the Mediterranean diet study still had heart attacks and strokes. So, he said, all the study showed was that “the Mediterranean diet and the horrible control diet were able to create disease in people who otherwise did not have it.”<br />
</blockquote><br />
<br />
10. Let us not forget the famous phrase popular among our forbearers: when it comes to diets, there are really only two: food and no food.<br />
<br />
Submitted by Paul Alper<br />
<br />
==Using genetic data without permission==<br />
<br />
[http://www.nytimes.com/2013/03/24/opinion/sunday/the-immortal-life-of-henrietta-lacks-the-sequel.html The Immortal Life of Henrietta Lacks, the Sequel], Rebecca Skloot, The New York Times, March 23, 2013.</div>Simon66217https://www.causeweb.org/wiki/chance/index.php?title=Chance_News_90&diff=16820Chance News 902013-01-04T21:46:25Z<p>Simon66217: </p>
<hr />
<div>==Quotations==<br />
“Early on election day, in two tight, tucked-away rooms at Obama headquarters …, the campaign's data-crunching team awaited the nation's first results, from Dixville Notch, a New Hampshire hamlet that traditionally votes at midnight.<br><br />
<br />
“Dixville Notch split 5-5. It did not seem an auspicious outcome for the president.<br><br />
<br />
[But t]heir model had gotten it right, predicting that about 50% of the village's voters were likely to support President Obama. …. And as the night wore on, swing state after swing state came in with results that were very close to the model's prediction. ….<br><br />
<br />
“To build the ‘support model,’ the campaign in 2011 made thousands of calls to voters — 5,000 to 10,000 in individual states, tens of thousands nationwide — to find out whether they backed the president. Then it analyzed what those voters had in common. More than 80 different pieces of information were factored in — including age, gender, voting history, home ownership and magazine subscriptions.”<br />
<div align=right>[http://www.latimes.com/news/nationworld/nation/la-na-obama-analytics-20121113,0,846342.story “Obama campaign's investment in data crunching paid off”]<br><br />
<i>Los Angeles Times</i>, November 13, 2012</div><br />
Submitted by Margaret Cibes<br />
<br />
----<br />
"Work. The best remedy for illness. One more reason why a person should never retire. The death rate among retired people is horrendous."<br />
<br />
<div align=right>Garrison Keillor, [http://prairiehome.publicradio.org/programs/2012/12/08/scripts/flu.shtml A Prairie Home Companion], 8 December 2012, The Town Hall Theatre, New York, NY</div><br />
(It's not a Forsooth, because Garrison Keillor knows...)<br />
<br />
Submitted by Jeanne Albert<br />
----<br />
“[Wharton School psychologist Uri] Simonsohn stressed that there’s a world of difference between data techniques that generate false positives, and fraud, but he said some academic psychologists have, until recently, been dangerously indifferent to both. .... Worse, sloppy statistics are “like steroids in baseball”: Throughout the affected fields, researchers who are too intellectually honest to use these tricks will publish less, and may perish. Meanwhile, the less fastidious flourish.<br />
<div align=right>[http://www.theatlantic.com/magazine/archive/2012/12/the-data-vigilante/309172/ “The Data Vigilante”], <i>The Atlantic</i>, December 2012</div><br />
Submitted by Margaret Cibes<br />
<br />
----<br />
"The most exciting phrase to hear in science, the one that heralds new discoveries, is not Eureka! (I found it!) but rather, 'hmm... that's funny...'"<br />
<div align=right>attributed to Isaac Asimov (1920-1992) at many websites</div><br />
For a bar chart of the frequency of Asimov’s publications over the period 1950-1995, see [http://www.asimovonline.com/oldsite/gifs/pub_graph.gif “Asimov’s Publications by Year”].<br><br />
<br />
Submitted by Margaret Cibes<br />
<br />
==Forsooth==<br />
<br />
[[File:121212_fox.jpg]]<br />
<br />
Look at the slope for changes of unemployment rate early in 2011 versus the change in slope for October to November.<br />
<br />
Source: http://freethoughtblogs.com/lousycanuck/files/2011/12/121212_fox.jpg<br />
<br />
This graphic is discussed at the [[http://freethoughtblogs.com/lousycanuck/2011/12/14/im-better-at-graphs-than-fox-news/121212_fox/ Freethought blogs]] and at the [[http://simplystatistics.org/2012/11/26/the-statisticians-at-fox-news-use-classic-and-novel-graphical-techniques-to-lead-with-data/ Simply Statistics blog]]<br />
<br />
Submitted by Steve Simon<br />
-----<br />
“I wonder if when you [Nate Silver] get up in the morning you open your kitchen cabinet and go, I’m feeling 18.5% Rice Chex and 27.9% Frosted Mini-Wheats and 32% one of those whole-grain Kashi cereals .... And then I wonder if you think, But I’m really feeling 58.3% like having a cupcake for breakfast ....”<br />
<div align=right>[http://www.newyorker.com/humor/2012/11/19/121119sh_shouts_rudnick “A Date With Nate”], <i>The New Yorker</i>, November 19, 2012</div><br />
Submitted by Margaret Cibes<br />
<br />
==The signal and the noise==<br />
<br />
The big winner in the 2012 election was not Barack Obama. It was Nate Silver, the statistics wunderkind of the fivethirtyeight.com blog. Do not be surprised if he is Time Magazine’s 2012 Man (Person? Geek? Nerd?) of the Year. Just before the 2012 election took place this is what Stephen Colbert in his role as a right-wing megalomaniac mockingly said about Silver’s ability to predict election outcomes:<br />
<br />
<blockquote><br />
Yes. This race is razor tight. That means no margin for error, or correct use of metaphor. I mean, it's banana up for grabs. But folks, every prediction out there needs a pooper. In this case, New York Times polling Jedi Nate Silver, who in 2008 correctly predicted 49 out of 50 states. But, you know what they say. '''Even a stopped clock is right 98% of the time.'''<br />
<br><br><br />
See, Silver's got a computer model that uses mumbo jumbo like "weighted polling average", "trendline adjustment", and "linear regression analysis", but ignores proven methodologies like flag-pin size, handshake strength, and intensity of debate glare.<br />
</blockquote><br />
While the gut feel of the “punditocracy” was certain the race would be very tight or that Romney would win in a landslide, Silver’s model based on his weighted averaging evaluation of the extensive polling, predicted the outcome (popular vote and electoral college vote) almost exactly. [http://www.washingtonpost.com/blogs/wonkblog/wp/2012/11/05/pundit-accountability-the-official-2012-election-prediction-thread/ Here] is a listing of what Silver and others predicted. The ''Washington Post'' had [http://www.washingtonpost.com/blogs/plum-line/post/what-nate-silver-really-accomplished/2012/11/21/6f9f10c2-3410-11e2-bfd5-e202b6d7b501_blog.html?hpid=z6 this description of Silver's achievement]:<br />
<blockquote><br />
...I believe people are seriously misstating what Silver achieved. It isn’t that he predicted the election right where others botched it. It’s that he popularized a way of thinking about polling, a way to navigate through conflicting numbers and speculation, that would still have remained invaluable even if he’d predicted the outcome wrong.<br />
<br><br><br />
Many liberals relied exclusively on Silver. But his model was only one of a number of polling trackers that were all worth consulting throughout — including Real Clear Politics, TPM, and HuffPollster — that were doing roughly the same thing: tracking averages of state polls.<br />
<br><br><br />
The election results have triggered soul-searching among pollsters, particularly those who got it wrong. But the failure of some polls to get it right doesn’t tell us anything we didn’t know before the election. Silver’s approach — and that of other modelers — has always been based on the idea that individual polls will inevitably be wrong. <br />
<br><br><br />
Silver’s accomplishment was to popularize tools enabling you to navigate the unavoidable reality that some individual polls will necessarily be off, thanks to methodology or chance. People keep saying Silver got it right because the polls did. But that’s not really true. The polling averages got it right.<br />
</blockquote><br />
<br />
Clearly, Silver never sleeps because all the while he was pumping out simulations of the presidential and US senate races, he published just before the election an amazing book, ''The Signal and the Noise: Why So Many Predictions Fail--but Some Don’t''. The reviews are glowingly positive as befits his track record. For instance, as <br />
[http://www.nytimes.com/2012/11/04/books/review/the-signal-and-the-noise-by-nate-silver.html?pagewanted=all Noam Scheiber] put it, “Nate Silver has lived a preposterously interesting life…It’s largely about evaluating predictions in a variety of fields, from finance to weather to epidemiology…Silver’s volume is more like an engagingly written user’s manual, with forays into topics like dynamic nonlinear systems (the guts of chaos theory) and Bayes’s theorem (a tool for figuring out how likely a particular hunch is right in light of the evidence we observe).” <br />
<br />
See also this [http://www.washingtonpost.com/opinions/the-signal-and-the-noise-why-so-many-predictions-fail--but-some-dont-by-nate-silver/2012/11/09/620bf2d0-0671-11e2-a10c-fa5a255a9258_story.html review of the the book] by John Allen Paulos.<br />
<br />
===Discussion===<br />
<br />
1. The above quotation from Scheiber failed to mention some other fascinating statistical prediction topics in the book: chess, poker, politics, basketball, earthquakes, flu outbreaks, cancer detection, terrorism and of course, baseball--Silver’s first success story. By all means, read the book which is both scholarly (56 pages of end notes) and breezy. However, because the book is so USA oriented, it may well be opaque to anyone outside of North America.<br />
<br />
2. The above link from the Washington Post has Silver claiming 332 electoral votes for Obama and 203 [misprint, should be 206] for Romney which turns out to be the exact result. However, on Silver’s blog itself, Obama gets only 313 electoral votes and Romney gets 225. Explain the discrepancy. Hint: Look at Silver’s prediction for Florida.<br />
<br />
3. The above link from the Washington Post indicates that several other poll aggregators using similar methodology were just as accurate as Silver. Speculate as to why they are less celebrated?<br />
<br />
4. Silver also predicted the outcome of the U.S. Senate races. In fact, while he got all the others right, he was quite wrong in one of them and spectacularly wrong in another. Which two were they? Speculate as to why Silver was less successful predicting the Senate races than he was on the presidential race.<br />
<br />
5. Silver’s use of averaging to improve a forecast has a long history in statistics. There exists [http://en.wikipedia.org/wiki/Francis_Galton a famous example of Francis Galton] of over 100 years ago:<br />
<blockquote><br />
In 1906, visiting a livestock fair, he stumbled upon an intriguing contest. An ox was on display, and the villagers were invited to guess the animal's weight after it was slaughtered and dressed. Nearly 800 participated, but not one person hit the exact mark: 1,198 pounds. Galton stated that "the middlemost estimate expresses the vox populi, every other estimate being condemned as too low or too high by a majority of the voters", and calculated this value (in modern terminology, the median) as 1,207 pounds. To his surprise, this was within 0.8% of the weight measured by the judges. Soon afterwards, he acknowledged that the mean of the guesses, at 1,197 pounds, was even more accurate.<br />
</blockquote><br />
Presumably, those 800 hundred villagers in 1906 knew something about oxen and pounds. Suppose Galton had asked the villagers to guess the number of chromosomes of the ox. Why in this case would averaging likely to be useless?<br />
<br />
6. Suppose instead, Galton had asked the villagers to come up with a number for the (putatively) [http://www.straightdope.com/columns/read/1008/did-medieval-scholars-argue-over-how-many-angels-could-dance-on-the-head-of-a-pin famous issue] of the medieval era: “How many angels can dance on the head of a pin?” Why is this different from inquiring about the weight of an ox or its number of chromosomes?<br />
<br />
7. Although Silver devotes many pages to the volatility of the stock market, he barely mentions (only in the footnote on page 368) Nassim Taleb and his “black swans.” Rather than black swans and fractals, Silver invokes the power-law distribution to explain “very occasional but very large swings up or down” in the stock market and the frequency of earthquakes. For more on the power-law distribution, see [http://en.wikipedia.org/wiki/Power_law this interesting Wikipedia article].<br />
<br />
8. One of the lessons of the book is that in order to predict a specific phenomenon successfully is that there needs to be a data rich environment. Therefore, ironically, weather forecasting is, so to speak, on much firmer ground than earthquake forecasting.<br />
<br />
9. Another lesson of the book is that when it comes to the game of poker, now that most of the poor players have left the scene, it is easier to make money by owning the house than being a participant. Knowledge of Bayes theorem can only go so far.<br />
<br />
Submitted by Paul Alper<br><br />
<br />
===Note===<br />
Readers might also like to view Nate Silver's two 5-minute appearances on Comedy Central's The Colbert Report, one on October 7, 2008[http://www.colbertnation.com/the-colbert-report-videos/187343/october-07-2008/nate-silver] and the other on November 5, 2012[http://www.colbertnation.com/the-colbert-report-videos/420765/november-05-2012/nate-silver].<br><br />
Submitted by Margaret Cibes<br><br />
<br />
==Internal polls==<br />
[http://fivethirtyeight.blogs.nytimes.com/2012/12/01/when-internal-polls-mislead-a-whole-campaign-may-be-to-blame/#more-37739 When internal polls mislead, a whole campaign may be to blame]<br><br />
by Nate Silver, FiveThirtyEight blog, ''New York Times'', 1 December 2012<br />
<br />
Silver presents the following chart that compares Romney internal polls with the actual results. <br />
<center><br />
[[File:InternalPoll.png]]<br />
</center><br />
He relies on Noam Scheiber's article from the ''New Republic'' (30 November 2012), [http://www.tnr.com/blog/plank/110597/exclusive-the-polls-made-mitt-romney-think-hed-win Exclusive: The internal polls that made Mitt Romney think he'd win].<br />
<br />
Silver suggests, "Campaigns might consider how pollsters are compensated; they could tie some of the pollster’s compensation to the accuracy of its final polls, for instance. ...But most important, campaigns would be wise not to have their pollsters serve as public spokesmen or spin doctors for the campaign. Campaigns have other personnel who specialize in those tasks..." <br />
<br />
In the same vein, Henry J. Enten concludes his lengthy article in the ''Guardian'', [http://www.guardian.co.uk/commentisfree/2012/nov/30/barack-obama-mitt-romney-polls-polling?INTCMP=SRCH Crunching the numbers shows how Obama and Romney were polls apart], with this statement:<br />
"Internal numbers that a campaign releases to the public should be thought of less as scientific surveys and more as talking points."<br />
<br />
Submitted by Paul Alper<br />
<br />
==Clinical trials need to be adapted to the Mayan calendar==<br />
<br />
[http://www.cmaj.ca/content/184/18/2021.full The Mayan Doomsday’s effect on survival outcomes in clinical trials] Paul Whetley-Price, Brian Hutton, Mark Clemons. CMAJ December 11, 2012 vol. 184 no. 18 doi: 10.1503/cmaj.121616.<br />
<br />
Will the world end when the Mayan calendar runs out on December 21, 2012? If so, we need to prepare.<br />
<br />
<blockquote> Such an event would undoubtedly affect population survival and, thus, survival outcomes in clinical trials. Here, we discuss how the outcomes of clinical trials may be affected by the extinction of all mankind and recommend appropriate changes to their conduct.</blockquote><br />
<br />
This paper presents [http://www.cmaj.ca/content/184/18/2021/F2.expansion.html a Kaplan-Meier curve] illustrating the effect of extinction of humankind, along with the gradual zombie repopulation.<br />
<br />
The authors go on to note that extinction will likely mask any mortality difference between two arms of a clinical trial and that it will make the recording of adverse event data impossible.<br />
<br />
===Questions===<br />
<br />
1. If you are a member of a DSMB monitoring a clinical trial, and the world ends, would that be sufficient grounds for stopping the trial early, or would you continue the trial to the planned endpoint in order to preserve the Type I error rate?<br />
<br />
2. Is death due to apocalypse considered an unexpected adverse event? If so, how quickly does it need to be reported?<br />
<br />
Submitted by Steve Simon<br />
<br />
==Use of technology in instruction==<br />
[http://www.sr.ithaka.org/research-publications/interactive-learning-online-public-universities-evidence-randomized-trials “Interactive Learning Online at Public Universities: Evidence from Randomized Trials”] (May 22, 2012) is a 50-page report about a formal statistical study of introductory statistics courses at 6 public universities. The control groups were enrolled in a traditional classroom course; the treatment groups were enrolled in a “hybrid course using a prototype machine-guided mode of instruction developed at Carnegie Mellon University in concert with one face-to-face meeting each week.” Lots of raw data is provided in tables and charts.<br><br />
<br />
The study was designed to answer the following questions about interactive online courses: (1) Can they maintain – or improve – improve basic learning outcomes? (2) Are they as – or more – effective for students in particular socio-economic groups? (3) Are they equally effective for less prepared students as for well prepared students?<br><br />
<br />
The authors conclude, “The results of this study are remarkable; they show comparable learning outcomes for this basic course, with a promise of cost savings and productivity gains over time. …. More research is needed.”<br><br />
<br />
Two of the authors wrote an earlier report in which they review the research literature about interactive online learning, [http://www.sr.ithaka.org/research-publications/current-status-research-online-learning-postsecondary-education “Current Status of Research on Online Learning in Postsecondary Education”] (May 18, 2012). They conclude, “The review yields little evidence to support broad claims that online or hybrid learning is significantly more effective or significantly less effective than courses taught in a face-to-face format. At the same time, it highlights the need for further research on this topic, with particular attention paid to differences in outcomes among different student populations and different sets of institutional users.”<br><br />
<br />
On the other hand, see an unrelated website, [http://teachingnaked.com/ Teaching Naked], where Jose Bowen offers a 22-minute video [http://teachingnaked.com/keynote-abstract/ “Keynote Abstract”], explaining his advocacy for the use of technology outside of the classroom.<br />
<blockquote>[T]he greatest value of a physical university will remain its face-to-face (naked) interaction between faculty and students. The most important benefits to using technology occur outside [Bowen’s emphasis] of the classroom. New technology can increase student preparation and engagement between classes and create more time for the in-class dialogue that makes the campus experience worth the extra money it will always cost to deliver. …. By rethinking our assignments, use of technology and course design, we can create more class time for the activities and interactions that most spark the critical thinking and change of mental models we seek.”</blockquote><br />
Chance readers can decide for themselves whether any of these pieces are relevant/worthwhile with respect to their experiences/opinions.<br><br />
<br />
Submitted by Margaret Cibes<br />
<br />
==Suggestions for savvier statistical reports==<br />
[http://online.wsj.com/article/SB10001424127887324669104578206201895302728.html?KEYWORDS=Carl+Bialik “Statistical Habits to Add, or Subtract, in 2013”]<br><br />
[http://blogs.wsj.com/numbersguy/tips-for-a-statistically-savvy-2013-1198/ “Tips for a Statistically Savvy 2013”]<br><br />
by Carl Bialik, <i>The Wall Street Journal</i>, December 28, 2012<br><br />
<br />
Bialik solicited from statisticians and other readers their pet peeves about statistical data as these are reported to the public. He also noted that 2013 has been designated the International Year of Statistics.<br><br />
<br />
One biostatistician stated, “The most important numerical fallacy is that people tend to think of numbers as known, constant and having no variability.”<br><br />
<br />
Based on his feedback, Bialik offers advice:<br><br />
(1) With respect to hasty conclusions, remember that things can look good from the standpoint of a small sample and/or in the short-term, but don’t ignore potential regression-to-the-mean effects.<br><br />
(2) Pay attention to the context of a statistical result: note the difference between relative versus absolute changes, between observational versus experimental studies, and between the absence of evidence versus the evidence of absence.<br><br />
(3) Realize that extremely unlikely events can occur, especially in very large populations.<br><br />
<br />
Other readers commented on their pet peeves:<br><br />
(a) rates “smoothed out to create arresting statistics,” such as “one murder every 10 minutes”;<br><br />
(b) charts which distort information, such as not starting the y-axis at 0 when possible;<br><br />
(c) lack of discussion of study design in medical research reports;<br><br />
(d) confusion between correlation and causation.<br><br />
<br />
One respondent urged consumers of statistical information not to underestimate the power of “simple computations.” He stated, “It’s amazing, even in our complex modern world, how many assertions fail simple ‘back of the envelope’ reasonable estimates with elementary computations.”<br><br />
<br />
There are many more issues raised by bloggers to Bialik’s second article. They identify two of my pet peeves – “nonsense such as ‘A earns ten times less than B’" and misuse of “percent” for “percentage point.” There is also a good list of “rules of thumb” that one blogger has compiled for his graduate students.<br><br />
<br />
Submitted by Margaret Cibes<br />
<br />
==Big data or big hype?==<br />
<br />
[http://www.nytimes.com/2012/12/30/technology/big-data-is-great-but-dont-forget-intuition.html Sure, Big Data Is Great. But So Is Intuition.] Steve Lohr, The New York Times, December 29, 2012.<br />
<br />
There are lots of people who will tell you all the wonderful things that Big Data will bring us. A recent research conference started off with some glowing testimonials.<br />
<br />
<blockquote> Andrew McAfee, principal research scientist at the M.I.T. Center for Digital Business, led off the conference by saying that Big Data would be “the next big chapter of our business history.” Next on stage was Erik Brynjolfsson, a professor and director of the M.I.T. center and a co-author of the article with Dr. McAfee. Big Data, said Professor Brynjolfsson, will “replace ideas, paradigms, organizations and ways of thinking about the world.” These drumroll claims rest on the premise that data like Web-browsing trails, sensor signals, GPS tracking, and social network messages will open the door to measuring and monitoring people and machines as never before. And by setting clever computer algorithms loose on the data troves, you can predict behavior of all kinds: shopping, dating and voting, for example. The results, according to technologists and business executives, will be a smarter world, with more efficient companies, better-served consumers and superior decisions guided by data and analysis. </blockquote><br />
<br />
Could it be possible though, that we are overhyping this a bit? Could there be some limits? It depends who you ask.<br />
<br />
<blockquote>At the M.I.T. conference, a panel was asked to cite examples of big failures in Big Data. No one could really think of any. Soon after, though, Roberto Rigobon could barely contain himself as he took to the stage. Mr. Rigobon, a professor at M.I.T.’s Sloan School of Management, said that the financial crisis certainly humbled the data hounds. “Hedge funds failed all over the world,” he said. </blockquote><br />
<br />
Models can be wrong of course. But do the people who fit these models to large data sets really appreciate this?<br />
<br />
<blockquote>Claudia Perlich, chief scientist at Media6Degrees, an online ad-targeting start-up in New York, puts the problem this way: “You can fool yourself with data like you can’t with anything else. I fear a Big Data bubble.” </blockquote><br />
<br />
Here's some sage advice.<br />
<br />
<blockquote>Thomas H. Davenport, a visiting professor at the Harvard Business School, is writing a book called “Keeping Up With the Quants” to help managers cope with the Big Data challenge. A major part of managing Big Data projects, he says, is asking the right questions: How do you define the problem? What data do you need? Where does it come from? What are the assumptions behind the model that the data is fed into? How is the model different from reality? </blockquote><br />
<br />
The article goes on to discuss some of the ethical dimensions of big data models.<br />
<br />
===Questions===<br />
<br />
1. Thinks of some reasons why the panel of experts on big data could not come up with any examples of failures. <br />
<br />
2. Is there something about models with big data that makes them more difficult to troubleshoot?<br />
<br />
Submitted by Steve Simon</div>Simon66217https://www.causeweb.org/wiki/chance/index.php?title=Chance_News_90&diff=16819Chance News 902013-01-04T21:44:20Z<p>Simon66217: /* Use of technology in instruction */</p>
<hr />
<div>==Quotations==<br />
“Early on election day, in two tight, tucked-away rooms at Obama headquarters …, the campaign's data-crunching team awaited the nation's first results, from Dixville Notch, a New Hampshire hamlet that traditionally votes at midnight.<br><br />
<br />
“Dixville Notch split 5-5. It did not seem an auspicious outcome for the president.<br><br />
<br />
[But t]heir model had gotten it right, predicting that about 50% of the village's voters were likely to support President Obama. …. And as the night wore on, swing state after swing state came in with results that were very close to the model's prediction. ….<br><br />
<br />
“To build the ‘support model,’ the campaign in 2011 made thousands of calls to voters — 5,000 to 10,000 in individual states, tens of thousands nationwide — to find out whether they backed the president. Then it analyzed what those voters had in common. More than 80 different pieces of information were factored in — including age, gender, voting history, home ownership and magazine subscriptions.”<br />
<div align=right>[http://www.latimes.com/news/nationworld/nation/la-na-obama-analytics-20121113,0,846342.story “Obama campaign's investment in data crunching paid off”]<br><br />
<i>Los Angeles Times</i>, November 13, 2012</div><br />
Submitted by Margaret Cibes<br />
<br />
----<br />
"Work. The best remedy for illness. One more reason why a person should never retire. The death rate among retired people is horrendous."<br />
<br />
<div align=right>Garrison Keillor, [http://prairiehome.publicradio.org/programs/2012/12/08/scripts/flu.shtml A Prairie Home Companion], 8 December 2012, The Town Hall Theatre, New York, NY</div><br />
(It's not a Forsooth, because Garrison Keillor knows...)<br />
<br />
Submitted by Jeanne Albert<br />
----<br />
“[Wharton School psychologist Uri] Simonsohn stressed that there’s a world of difference between data techniques that generate false positives, and fraud, but he said some academic psychologists have, until recently, been dangerously indifferent to both. .... Worse, sloppy statistics are “like steroids in baseball”: Throughout the affected fields, researchers who are too intellectually honest to use these tricks will publish less, and may perish. Meanwhile, the less fastidious flourish.<br />
<div align=right>[http://www.theatlantic.com/magazine/archive/2012/12/the-data-vigilante/309172/ “The Data Vigilante”], <i>The Atlantic</i>, December 2012</div><br />
Submitted by Margaret Cibes<br />
<br />
----<br />
"The most exciting phrase to hear in science, the one that heralds new discoveries, is not Eureka! (I found it!) but rather, 'hmm... that's funny...'"<br />
<div align=right>attributed to Isaac Asimov (1920-1992) at many websites</div><br />
For a bar chart of the frequency of Asimov’s publications over the period 1950-1995, see [http://www.asimovonline.com/oldsite/gifs/pub_graph.gif “Asimov’s Publications by Year”].<br><br />
<br />
Submitted by Margaret Cibes<br />
<br />
==Forsooth==<br />
<br />
[[File:121212_fox.jpg]]<br />
<br />
Look at the slope for changes of unemployment rate early in 2011 versus the change in slope for October to November.<br />
<br />
Source: http://freethoughtblogs.com/lousycanuck/files/2011/12/121212_fox.jpg<br />
<br />
This graphic is discussed at the [[http://freethoughtblogs.com/lousycanuck/2011/12/14/im-better-at-graphs-than-fox-news/121212_fox/ Freethought blogs]] and at the [[http://simplystatistics.org/2012/11/26/the-statisticians-at-fox-news-use-classic-and-novel-graphical-techniques-to-lead-with-data/ Simply Statistics blog]]<br />
<br />
Submitted by Steve Simon<br />
-----<br />
“I wonder if when you [Nate Silver] get up in the morning you open your kitchen cabinet and go, I’m feeling 18.5% Rice Chex and 27.9% Frosted Mini-Wheats and 32% one of those whole-grain Kashi cereals .... And then I wonder if you think, But I’m really feeling 58.3% like having a cupcake for breakfast ....”<br />
<div align=right>[http://www.newyorker.com/humor/2012/11/19/121119sh_shouts_rudnick “A Date With Nate”], <i>The New Yorker</i>, November 19, 2012</div><br />
Submitted by Margaret Cibes<br />
<br />
==The signal and the noise==<br />
<br />
The big winner in the 2012 election was not Barack Obama. It was Nate Silver, the statistics wunderkind of the fivethirtyeight.com blog. Do not be surprised if he is Time Magazine’s 2012 Man (Person? Geek? Nerd?) of the Year. Just before the 2012 election took place this is what Stephen Colbert in his role as a right-wing megalomaniac mockingly said about Silver’s ability to predict election outcomes:<br />
<br />
<blockquote><br />
Yes. This race is razor tight. That means no margin for error, or correct use of metaphor. I mean, it's banana up for grabs. But folks, every prediction out there needs a pooper. In this case, New York Times polling Jedi Nate Silver, who in 2008 correctly predicted 49 out of 50 states. But, you know what they say. '''Even a stopped clock is right 98% of the time.'''<br />
<br><br><br />
See, Silver's got a computer model that uses mumbo jumbo like "weighted polling average", "trendline adjustment", and "linear regression analysis", but ignores proven methodologies like flag-pin size, handshake strength, and intensity of debate glare.<br />
</blockquote><br />
While the gut feel of the “punditocracy” was certain the race would be very tight or that Romney would win in a landslide, Silver’s model based on his weighted averaging evaluation of the extensive polling, predicted the outcome (popular vote and electoral college vote) almost exactly. [http://www.washingtonpost.com/blogs/wonkblog/wp/2012/11/05/pundit-accountability-the-official-2012-election-prediction-thread/ Here] is a listing of what Silver and others predicted. The ''Washington Post'' had [http://www.washingtonpost.com/blogs/plum-line/post/what-nate-silver-really-accomplished/2012/11/21/6f9f10c2-3410-11e2-bfd5-e202b6d7b501_blog.html?hpid=z6 this description of Silver's achievement]:<br />
<blockquote><br />
...I believe people are seriously misstating what Silver achieved. It isn’t that he predicted the election right where others botched it. It’s that he popularized a way of thinking about polling, a way to navigate through conflicting numbers and speculation, that would still have remained invaluable even if he’d predicted the outcome wrong.<br />
<br><br><br />
Many liberals relied exclusively on Silver. But his model was only one of a number of polling trackers that were all worth consulting throughout — including Real Clear Politics, TPM, and HuffPollster — that were doing roughly the same thing: tracking averages of state polls.<br />
<br><br><br />
The election results have triggered soul-searching among pollsters, particularly those who got it wrong. But the failure of some polls to get it right doesn’t tell us anything we didn’t know before the election. Silver’s approach — and that of other modelers — has always been based on the idea that individual polls will inevitably be wrong. <br />
<br><br><br />
Silver’s accomplishment was to popularize tools enabling you to navigate the unavoidable reality that some individual polls will necessarily be off, thanks to methodology or chance. People keep saying Silver got it right because the polls did. But that’s not really true. The polling averages got it right.<br />
</blockquote><br />
<br />
Clearly, Silver never sleeps because all the while he was pumping out simulations of the presidential and US senate races, he published just before the election an amazing book, ''The Signal and the Noise: Why So Many Predictions Fail--but Some Don’t''. The reviews are glowingly positive as befits his track record. For instance, as <br />
[http://www.nytimes.com/2012/11/04/books/review/the-signal-and-the-noise-by-nate-silver.html?pagewanted=all Noam Scheiber] put it, “Nate Silver has lived a preposterously interesting life…It’s largely about evaluating predictions in a variety of fields, from finance to weather to epidemiology…Silver’s volume is more like an engagingly written user’s manual, with forays into topics like dynamic nonlinear systems (the guts of chaos theory) and Bayes’s theorem (a tool for figuring out how likely a particular hunch is right in light of the evidence we observe).” <br />
<br />
See also this [http://www.washingtonpost.com/opinions/the-signal-and-the-noise-why-so-many-predictions-fail--but-some-dont-by-nate-silver/2012/11/09/620bf2d0-0671-11e2-a10c-fa5a255a9258_story.html review of the the book] by John Allen Paulos.<br />
<br />
===Discussion===<br />
<br />
1. The above quotation from Scheiber failed to mention some other fascinating statistical prediction topics in the book: chess, poker, politics, basketball, earthquakes, flu outbreaks, cancer detection, terrorism and of course, baseball--Silver’s first success story. By all means, read the book which is both scholarly (56 pages of end notes) and breezy. However, because the book is so USA oriented, it may well be opaque to anyone outside of North America.<br />
<br />
2. The above link from the Washington Post has Silver claiming 332 electoral votes for Obama and 203 [misprint, should be 206] for Romney which turns out to be the exact result. However, on Silver’s blog itself, Obama gets only 313 electoral votes and Romney gets 225. Explain the discrepancy. Hint: Look at Silver’s prediction for Florida.<br />
<br />
3. The above link from the Washington Post indicates that several other poll aggregators using similar methodology were just as accurate as Silver. Speculate as to why they are less celebrated?<br />
<br />
4. Silver also predicted the outcome of the U.S. Senate races. In fact, while he got all the others right, he was quite wrong in one of them and spectacularly wrong in another. Which two were they? Speculate as to why Silver was less successful predicting the Senate races than he was on the presidential race.<br />
<br />
5. Silver’s use of averaging to improve a forecast has a long history in statistics. There exists [http://en.wikipedia.org/wiki/Francis_Galton a famous example of Francis Galton] of over 100 years ago:<br />
<blockquote><br />
In 1906, visiting a livestock fair, he stumbled upon an intriguing contest. An ox was on display, and the villagers were invited to guess the animal's weight after it was slaughtered and dressed. Nearly 800 participated, but not one person hit the exact mark: 1,198 pounds. Galton stated that "the middlemost estimate expresses the vox populi, every other estimate being condemned as too low or too high by a majority of the voters", and calculated this value (in modern terminology, the median) as 1,207 pounds. To his surprise, this was within 0.8% of the weight measured by the judges. Soon afterwards, he acknowledged that the mean of the guesses, at 1,197 pounds, was even more accurate.<br />
</blockquote><br />
Presumably, those 800 hundred villagers in 1906 knew something about oxen and pounds. Suppose Galton had asked the villagers to guess the number of chromosomes of the ox. Why in this case would averaging likely to be useless?<br />
<br />
6. Suppose instead, Galton had asked the villagers to come up with a number for the (putatively) [http://www.straightdope.com/columns/read/1008/did-medieval-scholars-argue-over-how-many-angels-could-dance-on-the-head-of-a-pin famous issue] of the medieval era: “How many angels can dance on the head of a pin?” Why is this different from inquiring about the weight of an ox or its number of chromosomes?<br />
<br />
7. Although Silver devotes many pages to the volatility of the stock market, he barely mentions (only in the footnote on page 368) Nassim Taleb and his “black swans.” Rather than black swans and fractals, Silver invokes the power-law distribution to explain “very occasional but very large swings up or down” in the stock market and the frequency of earthquakes. For more on the power-law distribution, see [http://en.wikipedia.org/wiki/Power_law this interesting Wikipedia article].<br />
<br />
8. One of the lessons of the book is that in order to predict a specific phenomenon successfully is that there needs to be a data rich environment. Therefore, ironically, weather forecasting is, so to speak, on much firmer ground than earthquake forecasting.<br />
<br />
9. Another lesson of the book is that when it comes to the game of poker, now that most of the poor players have left the scene, it is easier to make money by owning the house than being a participant. Knowledge of Bayes theorem can only go so far.<br />
<br />
Submitted by Paul Alper<br><br />
<br />
===Note===<br />
Readers might also like to view Nate Silver's two 5-minute appearances on Comedy Central's The Colbert Report, one on October 7, 2008[http://www.colbertnation.com/the-colbert-report-videos/187343/october-07-2008/nate-silver] and the other on November 5, 2012[http://www.colbertnation.com/the-colbert-report-videos/420765/november-05-2012/nate-silver].<br><br />
Submitted by Margaret Cibes<br><br />
<br />
==Internal polls==<br />
[http://fivethirtyeight.blogs.nytimes.com/2012/12/01/when-internal-polls-mislead-a-whole-campaign-may-be-to-blame/#more-37739 When internal polls mislead, a whole campaign may be to blame]<br><br />
by Nate Silver, FiveThirtyEight blog, ''New York Times'', 1 December 2012<br />
<br />
Silver presents the following chart that compares Romney internal polls with the actual results. <br />
<center><br />
[[File:InternalPoll.png]]<br />
</center><br />
He relies on Noam Scheiber's article from the ''New Republic'' (30 November 2012), [http://www.tnr.com/blog/plank/110597/exclusive-the-polls-made-mitt-romney-think-hed-win Exclusive: The internal polls that made Mitt Romney think he'd win].<br />
<br />
Silver suggests, "Campaigns might consider how pollsters are compensated; they could tie some of the pollster’s compensation to the accuracy of its final polls, for instance. ...But most important, campaigns would be wise not to have their pollsters serve as public spokesmen or spin doctors for the campaign. Campaigns have other personnel who specialize in those tasks..." <br />
<br />
In the same vein, Henry J. Enten concludes his lengthy article in the ''Guardian'', [http://www.guardian.co.uk/commentisfree/2012/nov/30/barack-obama-mitt-romney-polls-polling?INTCMP=SRCH Crunching the numbers shows how Obama and Romney were polls apart], with this statement:<br />
"Internal numbers that a campaign releases to the public should be thought of less as scientific surveys and more as talking points."<br />
<br />
Submitted by Paul Alper<br />
<br />
==Clinical trials need to be adapted to the Mayan calendar==<br />
<br />
[http://www.cmaj.ca/content/184/18/2021.full The Mayan Doomsday’s effect on survival outcomes in clinical trials] Paul Whetley-Price, Brian Hutton, Mark Clemons. CMAJ December 11, 2012 vol. 184 no. 18 doi: 10.1503/cmaj.121616.<br />
<br />
Will the world end when the Mayan calendar runs out on December 21, 2012? If so, we need to prepare.<br />
<br />
<blockquote> Such an event would undoubtedly affect population survival and, thus, survival outcomes in clinical trials. Here, we discuss how the outcomes of clinical trials may be affected by the extinction of all mankind and recommend appropriate changes to their conduct.</blockquote><br />
<br />
This paper presents [http://www.cmaj.ca/content/184/18/2021/F2.expansion.html a Kaplan-Meier curve] illustrating the effect of extinction of humankind, along with the gradual zombie repopulation.<br />
<br />
The authors go on to note that extinction will likely mask any mortality difference between two arms of a clinical trial and that it will make the recording of adverse event data impossible.<br />
<br />
===Questions===<br />
<br />
1. If you are a member of a DSMB monitoring a clinical trial, and the world ends, would that be sufficient grounds for stopping the trial early, or would you continue the trial to the planned endpoint in order to preserve the Type I error rate?<br />
<br />
2. Is death due to apocalypse considered an unexpected adverse event? If so, how quickly does it need to be reported?<br />
<br />
Submitted by Steve Simon<br />
<br />
==Use of technology in instruction==<br />
[http://www.sr.ithaka.org/research-publications/interactive-learning-online-public-universities-evidence-randomized-trials “Interactive Learning Online at Public Universities: Evidence from Randomized Trials”] (May 22, 2012) is a 50-page report about a formal statistical study of introductory statistics courses at 6 public universities. The control groups were enrolled in a traditional classroom course; the treatment groups were enrolled in a “hybrid course using a prototype machine-guided mode of instruction developed at Carnegie Mellon University in concert with one face-to-face meeting each week.” Lots of raw data is provided in tables and charts.<br><br />
<br />
The study was designed to answer the following questions about interactive online courses: (1) Can they maintain – or improve – improve basic learning outcomes? (2) Are they as – or more – effective for students in particular socio-economic groups? (3) Are they equally effective for less prepared students as for well prepared students?<br><br />
<br />
The authors conclude, “The results of this study are remarkable; they show comparable learning outcomes for this basic course, with a promise of cost savings and productivity gains over time. …. More research is needed.”<br><br />
<br />
Two of the authors wrote an earlier report in which they review the research literature about interactive online learning, [http://www.sr.ithaka.org/research-publications/current-status-research-online-learning-postsecondary-education “Current Status of Research on Online Learning in Postsecondary Education”] (May 18, 2012). They conclude, “The review yields little evidence to support broad claims that online or hybrid learning is significantly more effective or significantly less effective than courses taught in a face-to-face format. At the same time, it highlights the need for further research on this topic, with particular attention paid to differences in outcomes among different student populations and different sets of institutional users.”<br><br />
<br />
On the other hand, see an unrelated website, [http://teachingnaked.com/ Teaching Naked], where Jose Bowen offers a 22-minute video [http://teachingnaked.com/keynote-abstract/ “Keynote Abstract”], explaining his advocacy for the use of technology outside of the classroom.<br />
<blockquote>[T]he greatest value of a physical university will remain its face-to-face (naked) interaction between faculty and students. The most important benefits to using technology occur outside [Bowen’s emphasis] of the classroom. New technology can increase student preparation and engagement between classes and create more time for the in-class dialogue that makes the campus experience worth the extra money it will always cost to deliver. …. By rethinking our assignments, use of technology and course design, we can create more class time for the activities and interactions that most spark the critical thinking and change of mental models we seek.”</blockquote><br />
Chance readers can decide for themselves whether any of these pieces are relevant/worthwhile with respect to their experiences/opinions.<br><br />
<br />
Submitted by Margaret Cibes<br />
<br />
==Big data or big hype?==<br />
<br />
[http://www.nytimes.com/2012/12/30/technology/big-data-is-great-but-dont-forget-intuition.html Sure, Big Data Is Great. But So Is Intuition.] Steve Lohr, The New York Times, December 29, 2012.<br />
<br />
There are lots of people who will tell you all the wonderful things that Big Data will bring us. A recent research conference started off with some glowing testimonials.<br />
<br />
<blockquote> Andrew McAfee, principal research scientist at the M.I.T. Center for Digital Business, led off the conference by saying that Big Data would be “the next big chapter of our business history.” Next on stage was Erik Brynjolfsson, a professor and director of the M.I.T. center and a co-author of the article with Dr. McAfee. Big Data, said Professor Brynjolfsson, will “replace ideas, paradigms, organizations and ways of thinking about the world.” These drumroll claims rest on the premise that data like Web-browsing trails, sensor signals, GPS tracking, and social network messages will open the door to measuring and monitoring people and machines as never before. And by setting clever computer algorithms loose on the data troves, you can predict behavior of all kinds: shopping, dating and voting, for example. The results, according to technologists and business executives, will be a smarter world, with more efficient companies, better-served consumers and superior decisions guided by data and analysis. </blockquote><br />
<br />
Could it be possible though, that we are overhyping this a bit? Could there be some limits? It depends who you ask.<br />
<br />
<blockquote>At the M.I.T. conference, a panel was asked to cite examples of big failures in Big Data. No one could really think of any. Soon after, though, Roberto Rigobon could barely contain himself as he took to the stage. Mr. Rigobon, a professor at M.I.T.’s Sloan School of Management, said that the financial crisis certainly humbled the data hounds. “Hedge funds failed all over the world,” he said. </blockquote><br />
<br />
Models can be wrong of course. But do the people who fit these models to large data sets really appreciate this?<br />
<br />
<blockquote>Claudia Perlich, chief scientist at Media6Degrees, an online ad-targeting start-up in New York, puts the problem this way: “You can fool yourself with data like you can’t with anything else. I fear a Big Data bubble.” </blockquote><br />
<br />
Here's some sage advice.<br />
<br />
<blockquote>Thomas H. Davenport, a visiting professor at the Harvard Business School, is writing a book called “Keeping Up With the Quants” to help managers cope with the Big Data challenge. A major part of managing Big Data projects, he says, is asking the right questions: How do you define the problem? What data do you need? Where does it come from? What are the assumptions behind the model that the data is fed into? How is the model different from reality? </blockquote><br />
<br />
The article goes on to discuss some of the ethical dimensions of big data models.<br />
<br />
===Questions===<br />
<br />
1. Thinks of some reasons why the panel of experts on big data could not come up with any examples of failures. <br />
<br />
2. Is there something about models with big data that makes them more difficult to troubleshoot?<br />
<br />
Submitted by Steve Simon<br />
<br />
==Suggestions for savvier statistical reports==<br />
[http://online.wsj.com/article/SB10001424127887324669104578206201895302728.html?KEYWORDS=Carl+Bialik “Statistical Habits to Add, or Subtract, in 2013”]<br><br />
[http://blogs.wsj.com/numbersguy/tips-for-a-statistically-savvy-2013-1198/ “Tips for a Statistically Savvy 2013”]<br><br />
by Carl Bialik, <i>The Wall Street Journal</i>, December 28, 2012<br><br />
<br />
Bialik solicited from statisticians and other readers their pet peeves about statistical data as these are reported to the public. He also noted that 2013 has been designated the International Year of Statistics.<br><br />
<br />
One biostatistician stated, “The most important numerical fallacy is that people tend to think of numbers as known, constant and having no variability.”<br><br />
<br />
Based on his feedback, Bialik offers advice:<br><br />
(1) With respect to hasty conclusions, remember that things can look good from the standpoint of a small sample and/or in the short-term, but don’t ignore potential regression-to-the-mean effects.<br><br />
(2) Pay attention to the context of a statistical result: note the difference between relative versus absolute changes, between observational versus experimental studies, and between the absence of evidence versus the evidence of absence.<br><br />
(3) Realize that extremely unlikely events can occur, especially in very large populations.<br><br />
<br />
Other readers commented on their pet peeves:<br><br />
(a) rates “smoothed out to create arresting statistics,” such as “one murder every 10 minutes”;<br><br />
(b) charts which distort information, such as not starting the y-axis at 0 when possible;<br><br />
(c) lack of discussion of study design in medical research reports;<br><br />
(d) confusion between correlation and causation.<br><br />
<br />
One respondent urged consumers of statistical information not to underestimate the power of “simple computations.” He stated, “It’s amazing, even in our complex modern world, how many assertions fail simple ‘back of the envelope’ reasonable estimates with elementary computations.”<br><br />
<br />
There are many more issues raised by bloggers to Bialik’s second article. They identify two of my pet peeves – “nonsense such as ‘A earns ten times less than B’" and misuse of “percent” for “percentage point.” There is also a good list of “rules of thumb” that one blogger has compiled for his graduate students.<br><br />
<br />
Submitted by Margaret Cibes</div>Simon66217https://www.causeweb.org/wiki/chance/index.php?title=Chance_News_90&diff=16783Chance News 902012-12-14T17:45:43Z<p>Simon66217: /* Questions */</p>
<hr />
<div>==Quotations==<br />
“Early on election day, in two tight, tucked-away rooms at Obama headquarters …, the campaign's data-crunching team awaited the nation's first results, from Dixville Notch, a New Hampshire hamlet that traditionally votes at midnight.<br><br />
<br />
“Dixville Notch split 5-5. It did not seem an auspicious outcome for the president.<br><br />
<br />
[But t]heir model had gotten it right, predicting that about 50% of the village's voters were likely to support President Obama. …. And as the night wore on, swing state after swing state came in with results that were very close to the model's prediction. ….<br><br />
<br />
“To build the ‘support model,’ the campaign in 2011 made thousands of calls to voters — 5,000 to 10,000 in individual states, tens of thousands nationwide — to find out whether they backed the president. Then it analyzed what those voters had in common. More than 80 different pieces of information were factored in — including age, gender, voting history, home ownership and magazine subscriptions.”<br />
<div align=right>[http://www.latimes.com/news/nationworld/nation/la-na-obama-analytics-20121113,0,846342.story “Obama campaign's investment in data crunching paid off”]<br><br />
<i>Los Angeles Times</i>, November 13, 2012</div><br />
Submitted by Margaret Cibes<br />
<br />
----<br />
"Work. The best remedy for illness. One more reason why a person should never retire. The death rate among retired people is horrendous."<br />
<br />
<div align=right>Garrison Keillor, [http://prairiehome.publicradio.org/programs/2012/12/08/scripts/flu.shtml A Prairie Home Companion], 8 December 2012,<br />
The Town Hall Theatre, New York, NY</div><br />
<br />
(It's not a Forsooth, because Garrison Keillor knows...)<br />
<br />
Submitted by Jeanne Albert<br />
<br />
==Forsooth==<br />
<br />
[[File:121212_fox.jpg]]<br />
<br />
Look at the slope for changes of unemployment rate early in 2011 versus the change in slope for October to November.<br />
<br />
Source: http://freethoughtblogs.com/lousycanuck/files/2011/12/121212_fox.jpg<br />
<br />
This graphic is discussed at the [[http://freethoughtblogs.com/lousycanuck/2011/12/14/im-better-at-graphs-than-fox-news/121212_fox/ Freethought blogs]] and at the [[http://simplystatistics.org/2012/11/26/the-statisticians-at-fox-news-use-classic-and-novel-graphical-techniques-to-lead-with-data/ Simply Statistics blog]]<br />
<br />
Submitted by Steve Simon<br />
-----<br />
<br />
“I wonder if when you [Nate Silver] get up in the morning you open your kitchen cabinet and go, I’m feeling 18.5% Rice Chex and 27.9% Frosted Mini-Wheats and 32% one of those whole-grain Kashi cereals .... And then I wonder if you think, But I’m really feeling 58.3% like having a cupcake for breakfast ....”<br />
<div align=right>“A Date With Nate,” by Paul Rudnick<br><br />
<i>The New Yorker</i>, November 19, 2012</div><br />
Submitted by Margaret Cibes<br />
<br />
==The signal and the noise==<br />
<br />
The big winner in the 2012 election was not Barack Obama. It was Nate Silver, the statistics wunderkind of the fivethirtyeight.com blog. Do not be surprised if he is Time Magazine’s 2012 Man (Person? Geek? Nerd?) of the Year. Just before the 2012 election took place this is what Stephen Colbert in his role as a right-wing megalomaniac mockingly said about Silver’s ability to predict election outcomes:<br />
<br />
<blockquote><br />
Yes. This race is razor tight. That means no margin for error, or correct use of metaphor. I mean, it's banana up for grabs. But folks, every prediction out there needs a pooper. In this case, New York Times polling Jedi Nate Silver, who in 2008 correctly predicted 49 out of 50 states. But, you know what they say. '''Even a stopped clock is right 98% of the time.'''<br />
<br><br><br />
See, Silver's got a computer model that uses mumbo jumbo like "weighted polling average", "trendline adjustment", and "linear regression analysis", but ignores proven methodologies like flag-pin size, handshake strength, and intensity of debate glare.<br />
</blockquote><br />
While the gut feel of the “punditocracy” was certain the race would be very tight or that Romney would win in a landslide, Silver’s model based on his weighted averaging evaluation of the extensive polling, predicted the outcome (popular vote and electoral college vote) almost exactly. [http://www.washingtonpost.com/blogs/wonkblog/wp/2012/11/05/pundit-accountability-the-official-2012-election-prediction-thread/ Here] is a listing of what Silver and others predicted. The ''Washington Post'' had [http://www.washingtonpost.com/blogs/plum-line/post/what-nate-silver-really-accomplished/2012/11/21/6f9f10c2-3410-11e2-bfd5-e202b6d7b501_blog.html?hpid=z6 this description of Silver's achievement]:<br />
<blockquote><br />
...I believe people are seriously misstating what Silver achieved. It isn’t that he predicted the election right where others botched it. It’s that he popularized a way of thinking about polling, a way to navigate through conflicting numbers and speculation, that would still have remained invaluable even if he’d predicted the outcome wrong.<br />
<br><br><br />
Many liberals relied exclusively on Silver. But his model was only one of a number of polling trackers that were all worth consulting throughout — including Real Clear Politics, TPM, and HuffPollster — that were doing roughly the same thing: tracking averages of state polls.<br />
<br><br><br />
The election results have triggered soul-searching among pollsters, particularly those who got it wrong. But the failure of some polls to get it right doesn’t tell us anything we didn’t know before the election. Silver’s approach — and that of other modelers — has always been based on the idea that individual polls will inevitably be wrong. <br />
<br><br><br />
Silver’s accomplishment was to popularize tools enabling you to navigate the unavoidable reality that some individual polls will necessarily be off, thanks to methodology or chance. People keep saying Silver got it right because the polls did. But that’s not really true. The polling averages got it right.<br />
</blockquote><br />
<br />
Clearly, Silver never sleeps because all the while he was pumping out simulations of the presidential and US senate races, he published just before the election an amazing book, ''The Signal and the Noise: Why So Many Predictions Fail--but Some Don’t''. The reviews are glowingly positive as befits his track record. For instance, as <br />
[http://www.nytimes.com/2012/11/04/books/review/the-signal-and-the-noise-by-nate-silver.html?pagewanted=all Noam Scheiber] put it, “Nate Silver has lived a preposterously interesting life…It’s largely about evaluating predictions in a variety of fields, from finance to weather to epidemiology…Silver’s volume is more like an engagingly written user’s manual, with forays into topics like dynamic nonlinear systems (the guts of chaos theory) and Bayes’s theorem (a tool for figuring out how likely a particular hunch is right in light of the evidence we observe).” <br />
<br />
See also this [http://www.washingtonpost.com/opinions/the-signal-and-the-noise-why-so-many-predictions-fail--but-some-dont-by-nate-silver/2012/11/09/620bf2d0-0671-11e2-a10c-fa5a255a9258_story.html review of the the book] by John Allen Paulos.<br />
<br />
===Discussion===<br />
<br />
1. The above quotation from Scheiber failed to mention some other fascinating statistical prediction topics in the book: chess, poker, politics, basketball, earthquakes, flu outbreaks, cancer detection, terrorism and of course, baseball--Silver’s first success story. By all means, read the book which is both scholarly (56 pages of end notes) and breezy. However, because the book is so USA oriented, it may well be opaque to anyone outside of North America.<br />
<br />
2. The above link from the Washington Post has Silver claiming 332 electoral votes for Obama and 203 [misprint, should be 206] for Romney which turns out to be the exact result. However, on Silver’s blog itself, Obama gets only 313 electoral votes and Romney gets 225. Explain the discrepancy. Hint: Look at Silver’s prediction for Florida.<br />
<br />
3. The above link from the Washington Post indicates that several other poll aggregators using similar methodology were just as accurate as Silver. Speculate as to why they are less celebrated?<br />
<br />
4. Silver also predicted the outcome of the U.S. Senate races. In fact, while he got all the others right, he was quite wrong in one of them and spectacularly wrong in another. Which two were they? Speculate as to why Silver was less successful predicting the Senate races than he was on the presidential race.<br />
<br />
5. Silver’s use of averaging to improve a forecast has a long history in statistics. There exists [http://en.wikipedia.org/wiki/Francis_Galton a famous example of Francis Galton] of over 100 years ago:<br />
<blockquote><br />
In 1906, visiting a livestock fair, he stumbled upon an intriguing contest. An ox was on display, and the villagers were invited to guess the animal's weight after it was slaughtered and dressed. Nearly 800 participated, but not one person hit the exact mark: 1,198 pounds. Galton stated that "the middlemost estimate expresses the vox populi, every other estimate being condemned as too low or too high by a majority of the voters", and calculated this value (in modern terminology, the median) as 1,207 pounds. To his surprise, this was within 0.8% of the weight measured by the judges. Soon afterwards, he acknowledged that the mean of the guesses, at 1,197 pounds, was even more accurate.<br />
</blockquote><br />
Presumably, those 800 hundred villagers in 1906 knew something about oxen and pounds. Suppose Galton had asked the villagers to guess the number of chromosomes of the ox. Why in this case would averaging likely to be useless?<br />
<br />
6. Suppose instead, Galton had asked the villagers to come up with a number for the (putatively) [http://www.straightdope.com/columns/read/1008/did-medieval-scholars-argue-over-how-many-angels-could-dance-on-the-head-of-a-pin famous issue] of the medieval era: “How many angels can dance on the head of a pin?” Why is this different from inquiring about the weight of an ox or its number of chromosomes?<br />
<br />
7. Although Silver devotes many pages to the volatility of the stock market, he barely mentions (only in the footnote on page 368) Nassim Taleb and his “black swans.” Rather than black swans and fractals, Silver invokes the power-law distribution to explain “very occasional but very large swings up or down” in the stock market and the frequency of earthquakes. For more on the power-law distribution, see [http://en.wikipedia.org/wiki/Power_law this interesting Wikipedia article].<br />
<br />
8. One of the lessons of the book is that in order to predict a specific phenomenon successfully is that there needs to be a data rich environment. Therefore, ironically, weather forecasting is, so to speak, on much firmer ground than earthquake forecasting.<br />
<br />
9. Another lesson of the book is that when it comes to the game of poker, now that most of the poor players have left the scene, it is easier to make money by owning the house than being a participant. Knowledge of Bayes theorem can only go so far.<br />
<br />
Submitted by Paul Alper<br />
<br />
==Clinical trials need to be adapted to the Mayan calendar==<br />
<br />
[http://www.cmaj.ca/content/184/18/2021.full The Mayan Doomsday’s effect on survival outcomes in clinical trials] Paul Whetley-Price, Brian Hutton, Mark Clemons. CMAJ December 11, 2012 vol. 184 no. 18 doi: 10.1503/cmaj.121616.<br />
<br />
Will the world end when the Mayan calendar runs out on December 21, 2012? If so, we need to prepare.<br />
<br />
<blockquote> Such an event would undoubtedly affect population survival and, thus, survival outcomes in clinical trials. Here, we discuss how the outcomes of clinical trials may be affected by the extinction of all mankind and recommend appropriate changes to their conduct.</blockquote><br />
<br />
This paper presents [http://www.cmaj.ca/content/184/18/2021/F2.expansion.html a Kaplan-Meier curve] illustrating the effect of extinction of humankind, along with the gradual zombie repopulation.<br />
<br />
The authors go on to note that extinction will likely mask any mortality difference between two arms of a clinical trial and that it will make the recording of adverse event data impossible.<br />
<br />
===Questions===<br />
<br />
1. If you are a member of a DSMB monitoring a clinical trial, and the world ends, would that be sufficient grounds for stopping the trial early, or would you continue the trial to the planned endpoint in order to preserve the Type I error rate?<br />
<br />
2. Is death due to apocalypse considered an unexpected adverse event? If so, how quickly does it need to be reported?<br />
<br />
Submitted by Steve Simon</div>Simon66217https://www.causeweb.org/wiki/chance/index.php?title=Chance_News_90&diff=16782Chance News 902012-12-14T17:41:35Z<p>Simon66217: /* Item #2 */</p>
<hr />
<div>==Quotations==<br />
“Early on election day, in two tight, tucked-away rooms at Obama headquarters …, the campaign's data-crunching team awaited the nation's first results, from Dixville Notch, a New Hampshire hamlet that traditionally votes at midnight.<br><br />
<br />
“Dixville Notch split 5-5. It did not seem an auspicious outcome for the president.<br><br />
<br />
[But t]heir model had gotten it right, predicting that about 50% of the village's voters were likely to support President Obama. …. And as the night wore on, swing state after swing state came in with results that were very close to the model's prediction. ….<br><br />
<br />
“To build the ‘support model,’ the campaign in 2011 made thousands of calls to voters — 5,000 to 10,000 in individual states, tens of thousands nationwide — to find out whether they backed the president. Then it analyzed what those voters had in common. More than 80 different pieces of information were factored in — including age, gender, voting history, home ownership and magazine subscriptions.”<br />
<div align=right>[http://www.latimes.com/news/nationworld/nation/la-na-obama-analytics-20121113,0,846342.story “Obama campaign's investment in data crunching paid off”]<br><br />
<i>Los Angeles Times</i>, November 13, 2012</div><br />
Submitted by Margaret Cibes<br />
<br />
----<br />
"Work. The best remedy for illness. One more reason why a person should never retire. The death rate among retired people is horrendous."<br />
<br />
<div align=right>Garrison Keillor, [http://prairiehome.publicradio.org/programs/2012/12/08/scripts/flu.shtml A Prairie Home Companion], 8 December 2012,<br />
The Town Hall Theatre, New York, NY</div><br />
<br />
(It's not a Forsooth, because Garrison Keillor knows...)<br />
<br />
Submitted by Jeanne Albert<br />
<br />
==Forsooth==<br />
<br />
[[File:121212_fox.jpg]]<br />
<br />
Look at the slope for changes of unemployment rate early in 2011 versus the change in slope for October to November.<br />
<br />
Source: http://freethoughtblogs.com/lousycanuck/files/2011/12/121212_fox.jpg<br />
<br />
This graphic is discussed at the [[http://freethoughtblogs.com/lousycanuck/2011/12/14/im-better-at-graphs-than-fox-news/121212_fox/ Freethought blogs]] and at the [[http://simplystatistics.org/2012/11/26/the-statisticians-at-fox-news-use-classic-and-novel-graphical-techniques-to-lead-with-data/ Simply Statistics blog]]<br />
<br />
Submitted by Steve Simon<br />
-----<br />
<br />
“I wonder if when you [Nate Silver] get up in the morning you open your kitchen cabinet and go, I’m feeling 18.5% Rice Chex and 27.9% Frosted Mini-Wheats and 32% one of those whole-grain Kashi cereals .... And then I wonder if you think, But I’m really feeling 58.3% like having a cupcake for breakfast ....”<br />
<div align=right>“A Date With Nate,” by Paul Rudnick<br><br />
<i>The New Yorker</i>, November 19, 2012</div><br />
Submitted by Margaret Cibes<br />
<br />
==The signal and the noise==<br />
<br />
The big winner in the 2012 election was not Barack Obama. It was Nate Silver, the statistics wunderkind of the fivethirtyeight.com blog. Do not be surprised if he is Time Magazine’s 2012 Man (Person? Geek? Nerd?) of the Year. Just before the 2012 election took place this is what Stephen Colbert in his role as a right-wing megalomaniac mockingly said about Silver’s ability to predict election outcomes:<br />
<br />
<blockquote><br />
Yes. This race is razor tight. That means no margin for error, or correct use of metaphor. I mean, it's banana up for grabs. But folks, every prediction out there needs a pooper. In this case, New York Times polling Jedi Nate Silver, who in 2008 correctly predicted 49 out of 50 states. But, you know what they say. '''Even a stopped clock is right 98% of the time.'''<br />
<br><br><br />
See, Silver's got a computer model that uses mumbo jumbo like "weighted polling average", "trendline adjustment", and "linear regression analysis", but ignores proven methodologies like flag-pin size, handshake strength, and intensity of debate glare.<br />
</blockquote><br />
While the gut feel of the “punditocracy” was certain the race would be very tight or that Romney would win in a landslide, Silver’s model based on his weighted averaging evaluation of the extensive polling, predicted the outcome (popular vote and electoral college vote) almost exactly. [http://www.washingtonpost.com/blogs/wonkblog/wp/2012/11/05/pundit-accountability-the-official-2012-election-prediction-thread/ Here] is a listing of what Silver and others predicted. The ''Washington Post'' had [http://www.washingtonpost.com/blogs/plum-line/post/what-nate-silver-really-accomplished/2012/11/21/6f9f10c2-3410-11e2-bfd5-e202b6d7b501_blog.html?hpid=z6 this description of Silver's achievement]:<br />
<blockquote><br />
...I believe people are seriously misstating what Silver achieved. It isn’t that he predicted the election right where others botched it. It’s that he popularized a way of thinking about polling, a way to navigate through conflicting numbers and speculation, that would still have remained invaluable even if he’d predicted the outcome wrong.<br />
<br><br><br />
Many liberals relied exclusively on Silver. But his model was only one of a number of polling trackers that were all worth consulting throughout — including Real Clear Politics, TPM, and HuffPollster — that were doing roughly the same thing: tracking averages of state polls.<br />
<br><br><br />
The election results have triggered soul-searching among pollsters, particularly those who got it wrong. But the failure of some polls to get it right doesn’t tell us anything we didn’t know before the election. Silver’s approach — and that of other modelers — has always been based on the idea that individual polls will inevitably be wrong. <br />
<br><br><br />
Silver’s accomplishment was to popularize tools enabling you to navigate the unavoidable reality that some individual polls will necessarily be off, thanks to methodology or chance. People keep saying Silver got it right because the polls did. But that’s not really true. The polling averages got it right.<br />
</blockquote><br />
<br />
Clearly, Silver never sleeps because all the while he was pumping out simulations of the presidential and US senate races, he published just before the election an amazing book, ''The Signal and the Noise: Why So Many Predictions Fail--but Some Don’t''. The reviews are glowingly positive as befits his track record. For instance, as <br />
[http://www.nytimes.com/2012/11/04/books/review/the-signal-and-the-noise-by-nate-silver.html?pagewanted=all Noam Scheiber] put it, “Nate Silver has lived a preposterously interesting life…It’s largely about evaluating predictions in a variety of fields, from finance to weather to epidemiology…Silver’s volume is more like an engagingly written user’s manual, with forays into topics like dynamic nonlinear systems (the guts of chaos theory) and Bayes’s theorem (a tool for figuring out how likely a particular hunch is right in light of the evidence we observe).” <br />
<br />
See also this [http://www.washingtonpost.com/opinions/the-signal-and-the-noise-why-so-many-predictions-fail--but-some-dont-by-nate-silver/2012/11/09/620bf2d0-0671-11e2-a10c-fa5a255a9258_story.html review of the the book] by John Allen Paulos.<br />
<br />
===Discussion===<br />
<br />
1. The above quotation from Scheiber failed to mention some other fascinating statistical prediction topics in the book: chess, poker, politics, basketball, earthquakes, flu outbreaks, cancer detection, terrorism and of course, baseball--Silver’s first success story. By all means, read the book which is both scholarly (56 pages of end notes) and breezy. However, because the book is so USA oriented, it may well be opaque to anyone outside of North America.<br />
<br />
2. The above link from the Washington Post has Silver claiming 332 electoral votes for Obama and 203 [misprint, should be 206] for Romney which turns out to be the exact result. However, on Silver’s blog itself, Obama gets only 313 electoral votes and Romney gets 225. Explain the discrepancy. Hint: Look at Silver’s prediction for Florida.<br />
<br />
3. The above link from the Washington Post indicates that several other poll aggregators using similar methodology were just as accurate as Silver. Speculate as to why they are less celebrated?<br />
<br />
4. Silver also predicted the outcome of the U.S. Senate races. In fact, while he got all the others right, he was quite wrong in one of them and spectacularly wrong in another. Which two were they? Speculate as to why Silver was less successful predicting the Senate races than he was on the presidential race.<br />
<br />
5. Silver’s use of averaging to improve a forecast has a long history in statistics. There exists [http://en.wikipedia.org/wiki/Francis_Galton a famous example of Francis Galton] of over 100 years ago:<br />
<blockquote><br />
In 1906, visiting a livestock fair, he stumbled upon an intriguing contest. An ox was on display, and the villagers were invited to guess the animal's weight after it was slaughtered and dressed. Nearly 800 participated, but not one person hit the exact mark: 1,198 pounds. Galton stated that "the middlemost estimate expresses the vox populi, every other estimate being condemned as too low or too high by a majority of the voters", and calculated this value (in modern terminology, the median) as 1,207 pounds. To his surprise, this was within 0.8% of the weight measured by the judges. Soon afterwards, he acknowledged that the mean of the guesses, at 1,197 pounds, was even more accurate.<br />
</blockquote><br />
Presumably, those 800 hundred villagers in 1906 knew something about oxen and pounds. Suppose Galton had asked the villagers to guess the number of chromosomes of the ox. Why in this case would averaging likely to be useless?<br />
<br />
6. Suppose instead, Galton had asked the villagers to come up with a number for the (putatively) [http://www.straightdope.com/columns/read/1008/did-medieval-scholars-argue-over-how-many-angels-could-dance-on-the-head-of-a-pin famous issue] of the medieval era: “How many angels can dance on the head of a pin?” Why is this different from inquiring about the weight of an ox or its number of chromosomes?<br />
<br />
7. Although Silver devotes many pages to the volatility of the stock market, he barely mentions (only in the footnote on page 368) Nassim Taleb and his “black swans.” Rather than black swans and fractals, Silver invokes the power-law distribution to explain “very occasional but very large swings up or down” in the stock market and the frequency of earthquakes. For more on the power-law distribution, see [http://en.wikipedia.org/wiki/Power_law this interesting Wikipedia article].<br />
<br />
8. One of the lessons of the book is that in order to predict a specific phenomenon successfully is that there needs to be a data rich environment. Therefore, ironically, weather forecasting is, so to speak, on much firmer ground than earthquake forecasting.<br />
<br />
9. Another lesson of the book is that when it comes to the game of poker, now that most of the poor players have left the scene, it is easier to make money by owning the house than being a participant. Knowledge of Bayes theorem can only go so far.<br />
<br />
Submitted by Paul Alper<br />
<br />
==Clinical trials need to be adapted to the Mayan calendar==<br />
<br />
[http://www.cmaj.ca/content/184/18/2021.full The Mayan Doomsday’s effect on survival outcomes in clinical trials] Paul Whetley-Price, Brian Hutton, Mark Clemons. CMAJ December 11, 2012 vol. 184 no. 18 doi: 10.1503/cmaj.121616.<br />
<br />
Will the world end when the Mayan calendar runs out on December 21, 2012? If so, we need to prepare.<br />
<br />
<blockquote> Such an event would undoubtedly affect population survival and, thus, survival outcomes in clinical trials. Here, we discuss how the outcomes of clinical trials may be affected by the extinction of all mankind and recommend appropriate changes to their conduct.</blockquote><br />
<br />
This paper presents [http://www.cmaj.ca/content/184/18/2021/F2.expansion.html a Kaplan-Meier curve] illustrating the effect of extinction of humankind, along with the gradual zombie repopulation.<br />
<br />
The authors go on to note that extinction will likely mask any mortality difference between two arms of a clinical trial and that it will make the recording of adverse event data impossible.<br />
<br />
===Questions===<br />
<br />
1. If you are a member of a DSMB monitoring a clinical trial, and the world ends, would that be sufficient grounds for stopping the trial early, or would you continue the trial to the planned endpoint in order to preserve the Type I error rate?<br />
<br />
2. Is death due to apocalypse considered an unexpected adverse event? If so, how quickly does it need to be reported?</div>Simon66217https://www.causeweb.org/wiki/chance/index.php?title=Chance_News_90&diff=16774Chance News 902012-11-29T20:42:35Z<p>Simon66217: /* Forsooth */</p>
<hr />
<div>==Quotations==<br />
“Early on election day, in two tight, tucked-away rooms at Obama headquarters …, the campaign's data-crunching team awaited the nation's first results, from Dixville Notch, a New Hampshire hamlet that traditionally votes at midnight.<br><br />
<br />
“Dixville Notch split 5-5. It did not seem an auspicious outcome for the president.<br><br />
<br />
[But t]heir model had gotten it right, predicting that about 50% of the village's voters were likely to support President Obama. …. And as the night wore on, swing state after swing state came in with results that were very close to the model's prediction. ….<br><br />
<br />
“To build the ‘support model,’ the campaign in 2011 made thousands of calls to voters — 5,000 to 10,000 in individual states, tens of thousands nationwide — to find out whether they backed the president. Then it analyzed what those voters had in common. More than 80 different pieces of information were factored in — including age, gender, voting history, home ownership and magazine subscriptions.”<br />
<div align=right>[http://www.latimes.com/news/nationworld/nation/la-na-obama-analytics-20121113,0,846342.story “Obama campaign's investment in data crunching paid off”]<br><br />
<i>Los Angeles Times</i>, November 13, 2012</div><br />
Submitted by Margaret Cibes<br />
<br />
==Forsooth==<br />
<br />
[[File:121212_fox.jpg]]<br />
<br />
Look at the slope for changes of unemployment rate early in 2011 versus the change in slope for October to November.<br />
<br />
Source: http://freethoughtblogs.com/lousycanuck/files/2011/12/121212_fox.jpg<br />
<br />
This graphic is discussed at the [[http://freethoughtblogs.com/lousycanuck/2011/12/14/im-better-at-graphs-than-fox-news/121212_fox/ Freethought blogs]] and at the [[http://simplystatistics.org/2012/11/26/the-statisticians-at-fox-news-use-classic-and-novel-graphical-techniques-to-lead-with-data/ Simply Statistics blog]]<br />
<br />
Submitted by Steve Simon<br />
<br />
==The signal and the noise==<br />
<br />
The big winner in the 2012 election was not Barack Obama. It was Nate Silver, the statistics wunderkind of the fivethirtyeight.com blog. Do not be surprised if he is Time Magazine’s 2012 Man (Person? Geek? Nerd?) of the Year. Just before the 2012 election took place this is what Stephen Colbert in his role as a right-wing megalomaniac mockingly said about Silver’s ability to predict election outcomes:<br />
<br />
<blockquote><br />
Yes. This race is razor tight. That means no margin for error, or correct use of metaphor. I mean, it's banana up for grabs. But folks, every prediction out there needs a pooper. In this case, New York Times polling Jedi Nate Silver, who in 2008 correctly predicted 49 out of 50 states. But, you know what they say. '''Even a stopped clock is right 98% of the time.'''<br />
<br><br><br />
See, Silver's got a computer model that uses mumbo jumbo like "weighted polling average", "trendline adjustment", and "linear regression analysis", but ignores proven methodologies like flag-pin size, handshake strength, and intensity of debate glare.<br />
</blockquote><br />
While the gut feel of the “punditocracy” was certain the race would be very tight or that Romney would win in a landslide, Silver’s model based on his weighted averaging evaluation of the extensive polling, predicted the outcome (popular vote and electoral college vote) almost exactly. [http://www.washingtonpost.com/blogs/wonkblog/wp/2012/11/05/pundit-accountability-the-official-2012-election-prediction-thread/ Here] is a listing of what Silver and others predicted. The ''Washington Post'' had [http://www.washingtonpost.com/blogs/plum-line/post/what-nate-silver-really-accomplished/2012/11/21/6f9f10c2-3410-11e2-bfd5-e202b6d7b501_blog.html?hpid=z6 this description of Silver's achievement]:<br />
<blockquote><br />
...I believe people are seriously misstating what Silver achieved. It isn’t that he predicted the election right where others botched it. It’s that he popularized a way of thinking about polling, a way to navigate through conflicting numbers and speculation, that would still have remained invaluable even if he’d predicted the outcome wrong.<br />
<br><br><br />
Many liberals relied exclusively on Silver. But his model was only one of a number of polling trackers that were all worth consulting throughout — including Real Clear Politics, TPM, and HuffPollster — that were doing roughly the same thing: tracking averages of state polls.<br />
<br><br><br />
The election results have triggered soul-searching among pollsters, particularly those who got it wrong. But the failure of some polls to get it right doesn’t tell us anything we didn’t know before the election. Silver’s approach — and that of other modelers — has always been based on the idea that individual polls will inevitably be wrong. <br />
<br><br><br />
Silver’s accomplishment was to popularize tools enabling you to navigate the unavoidable reality that some individual polls will necessarily be off, thanks to methodology or chance. People keep saying Silver got it right because the polls did. But that’s not really true. The polling averages got it right.<br />
</blockquote><br />
<br />
Clearly, Silver never sleeps because all the while he was pumping out simulations of the presidential and US senate races, he published just before the election an amazing book, ''The Signal and the Noise: Why So Many Predictions Fail--but Some Don’t''. The reviews are glowingly positive as befits his track record. For instance, as <br />
[http://www.nytimes.com/2012/11/04/books/review/the-signal-and-the-noise-by-nate-silver.html?pagewanted=all Noam Scheiber] put it, “Nate Silver has lived a preposterously interesting life…It’s largely about evaluating predictions in a variety of fields, from finance to weather to epidemiology…Silver’s volume is more like an engagingly written user’s manual, with forays into topics like dynamic nonlinear systems (the guts of chaos theory) and Bayes’s theorem (a tool for figuring out how likely a particular hunch is right in light of the evidence we observe).” <br />
<br />
See also this [http://www.washingtonpost.com/opinions/the-signal-and-the-noise-why-so-many-predictions-fail--but-some-dont-by-nate-silver/2012/11/09/620bf2d0-0671-11e2-a10c-fa5a255a9258_story.html review of the the book] by John Allen Paulos.<br />
<br />
===Discussion===<br />
<br />
1. The above quotation from Scheiber failed to mention some other fascinating statistical prediction topics in the book: chess, poker, politics, basketball, earthquakes, flu outbreaks, cancer detection, terrorism and of course, baseball--Silver’s first success story. By all means, read the book which is both scholarly (56 pages of end notes) and breezy. However, because the book is so USA oriented, it may well be opaque to anyone outside of North America.<br />
<br />
2. The above link from the Washington Post has Silver claiming 332 electoral votes for Obama and 203 [misprint, should be 206] for Romney which turns out to be the exact result. However, on Silver’s blog itself, Obama gets only 313 electoral votes and Romney gets 225. Explain the discrepancy. Hint: Look at Silver’s prediction for Florida.<br />
<br />
3. The above link from the Washington Post indicates that several other poll aggregators using similar methodology were just as accurate as Silver. Speculate as to why they are less celebrated?<br />
<br />
4. Silver also predicted the outcome of the U.S. Senate races. In fact, while he got all the others right, he was quite wrong in one of them and spectacularly wrong in another. Which two were they? Speculate as to why Silver was less successful predicting the Senate races than he was on the presidential race.<br />
<br />
5. Silver’s use of averaging to improve a forecast has a long history in statistics. There exists [http://en.wikipedia.org/wiki/Francis_Galton a famous example of Francis Galton] of over 100 years ago:<br />
<blockquote><br />
In 1906, visiting a livestock fair, he stumbled upon an intriguing contest. An ox was on display, and the villagers were invited to guess the animal's weight after it was slaughtered and dressed. Nearly 800 participated, but not one person hit the exact mark: 1,198 pounds. Galton stated that "the middlemost estimate expresses the vox populi, every other estimate being condemned as too low or too high by a majority of the voters", and calculated this value (in modern terminology, the median) as 1,207 pounds. To his surprise, this was within 0.8% of the weight measured by the judges. Soon afterwards, he acknowledged that the mean of the guesses, at 1,197 pounds, was even more accurate.<br />
</blockquote><br />
Presumably, those 800 hundred villagers in 1906 knew something about oxen and pounds. Suppose Galton had asked the villagers to guess the number of chromosomes of the ox. Why in this case would averaging likely to be useless?<br />
<br />
6. Suppose instead, Galton had asked the villagers to come up with a number for the (putatively) [http://www.straightdope.com/columns/read/1008/did-medieval-scholars-argue-over-how-many-angels-could-dance-on-the-head-of-a-pin famous issue] of the medieval era: “How many angels can dance on the head of a pin?” Why is this different from inquiring about the weight of an ox or its number of chromosomes?<br />
<br />
7. Although Silver devotes many pages to the volatility of the stock market, he barely mentions (only in the footnote on page 368) Nassim Taleb and his “black swans.” Rather than black swans and fractals, Silver invokes the power-law distribution to explain “very occasional but very large swings up or down” in the stock market and the frequency of earthquakes. For more on the power-law distribution, see [http://en.wikipedia.org/wiki/Power_law this interesting Wikipedia article].<br />
<br />
8. One of the lessons of the book is that in order to predict a specific phenomenon successfully is that there needs to be a data rich environment. Therefore, ironically, weather forecasting is, so to speak, on much firmer ground than earthquake forecasting.<br />
<br />
9. Another lesson of the book is that when it comes to the game of poker, now that most of the poor players have left the scene, it is easier to make money by owning the house than being a participant. Knowledge of Bayes theorem can only go so far.<br />
<br />
Submitted by Paul Alper<br />
<br />
==Item #2==</div>Simon66217https://www.causeweb.org/wiki/chance/index.php?title=File:121212_fox.jpg&diff=16773File:121212 fox.jpg2012-11-29T20:32:01Z<p>Simon66217: </p>
<hr />
<div></div>Simon66217https://www.causeweb.org/wiki/chance/index.php?title=Chance_News_89&diff=16619Chance News 892012-11-02T21:19:51Z<p>Simon66217: /* Discussion */</p>
<hr />
<div>==Quotations==<br />
"To rephrase Winston Churchill: Polls are the worst form of measuring public opinion — except for all of the others."<br />
<div align=right>--Humphrey Taylor, Chairman of The Harris Poll,<br />
[http://www.nytimes.com/2012/10/25/opinion/political-polls-influential-imperfect-and-everywhere.html Letter to the Editor], ''New York Times'', 24 October 2012 </div><br />
<br />
-----<br />
"We found that almost exactly half of the predictions [by the McLaughlin Group TV pundits in 2008] were right, and almost exactly half were wrong, meaning if you'd just flipped a coin instead of listening to these guys and girls, you would have done just as well. …. One of them, actually … said she thought McCain would win by half a point. Of course, what happened the next week where she came back on the air and said, 'Oh, Obama's win had been inevitable, how could he lose with the economy' ... so there's not really a lot of accountability."<br><br />
<div align=right>Nate Silver in [http://www.npr.org/2012/10/10/162594751/signal-and-noise-prediction-as-art-and-science “‘Signal’ And ‘Noise’: Prediction As Art And Science”]<br><br />
NPR, 10 October 2012</div><br />
<br />
Re media punditry: “‘Gut.’ ‘Momentum.’ ‘Who knows?’ ‘Maybe.’ Every word of that carefully hedged, adding up to a giant nothing-burger. But these are the kinds of analyses that earn you pundit cred. They're ‘smart takes.’ Meanwhile Nate Silver is being raked over the coals for committing the sin of showing his math.”<br />
<div align=right>Simon Maloy in [http://mediamatters.org/blog/2012/10/29/pundits-vs-nate-silver-data-vs-gut/191001 “Pundits Vs. Nate Silver, Data Vs. ‘Gut’"]<br><br />
<i>Media Matters</i>, October 29, 2012</div><br />
<br />
“The correlation phrase has become so common [on Internet blogs] and so irritating that a minor backlash has now ensued against the rhetoric if not the concept. No, correlation does not imply causation, but it sure as hell provides a hint. …. But there's still another puzzle in the phrase. To say that correlation does not imply causation makes an important point about the limits of statistics, but there are other limits, too, and ones that scientists ignore with far more frequency. In <i>The Cult of Statistical Significance</i>, the economists … cite one of these and make an impassioned, book-length argument against the arbitrary cutoff [5 %] that decides which experimental findings count and which ones don't.”<br><br />
<div align=right>Daniel Engberg in [http://www.slate.com/articles/health_and_science/science/2012/10/correlation_does_not_imply_causation_how_the_internet_fell_in_love_with_a_stats_class_clich_.single.html “The Internet Blowhard’s Favorite Phrase”]<br><br />
<i>Slate</i>, October 2, 2012</div><br />
Submitted by Margaret Cibes<br />
<br />
==Forsooth==<br />
"Picture yourself behind the wheel on a dark and shadowy night, watching the windshield wipers bat away the rain and wondering, 'What are the odds I’m going to hit a deer?' The answer would be one in 102 if you live in Virginia."<br />
<br />
<div align=right>[http://www.washingtonpost.com/local/trafficandcommuting/odds-of-striking-deer-high-in-maryland-virginia/2012/10/25/69f90f20-1e02-11e2-9cd5-b55c38388962_story.html Odds of striking deer high in Maryland, Virginia], ''Washington Post'', 25 October 2012 </div><br />
<br />
Thanks to Paul Alper for suggesting this story (see more [http://test.causeweb.org/wiki/chance/index.php/Chance_News_89#Drivers_beware_of_deer below]).<br />
<br />
-----<br />
"The main philosophical question is, how should [the recession] be treated? … Should it be treated as an outlier and done away with?"<br />
<br />
<div align=right>Chief statistician at Bureau Economic Analysis<br><br />
quoted by Carl Bialik in [http://online.wsj.com/article/SB10001424052970203897404578078971064492256.html?KEYWORDS=carl+bialik “Economists’ Goal: A Measure for All Seasons]<br><br />
<i>The Wall Street Journal</i>, October 26, 2012</div><br />
Submitted by Margaret Cibes<br />
<br />
==Simpson's paradox on Car Talk==<br />
[http://www.cartalk.com/content/take-ray-out-ball-game?question Take Ray out to the ball game...]<br><br />
Car Talk Puzzler, NPR, 22 September 2012<br />
<br />
Here is the puzzle: Popeye batted .250 for before the All-Star break, while Bluto batted .300; Popeye batted .375 after the All-Star break, while Bluto batted .400. So how did Popeye win his bet that he would have the better average for the season? Statistically minded listeners will quickly recognize this as an instance of Simpson's Paradox. Still, everything sounds like more fun when Tom and Ray discuss it! You can read their solution [http://www.cartalk.com/content/take-ray-out-ball-game?answer here].<br />
<br />
A famous real-life example of Simpson's Paradox with batting averages can be found [http://en.wikipedia.org/wiki/Simpson's_paradox#Batting_averages here].<br />
<br />
==Sleep and fat==<br />
[http://www.scientificamerican.com/podcast/episode.cfm?id=your-fat-needs-sleep-too-12-10-16 Your fat needs sleep, too]<br><br />
by Katherine Harmon, ''Scientific American'', 16 October 2012<br />
<br />
As described in the article (actually the transcript from a "60-Second Health" podcast--you can also listen at the link above):<br />
<blockquote><br />
Sleep is good for you. Getting by on too little sleep increases the risk for heart disease, stroke, high blood pressure, diabetes and other illnesses. It also makes it harder to lose weight or stay slim because sleep deprivation makes you hungrier and less likely to be active during the day.<br />
</blockquote><br />
<br />
Further,<br />
<br />
<blockquote><br />
Now, research shows that sleep also affects fat cells. Our fat cells play an important role in regulating energy use and storage, including insulin processing.<br />
</blockquote><br />
<br />
The research referred to, a randomized crossover study, can be found [http://annals.org/article.aspx?articleid=1379773 in an article] by Josiane Broussard et al. Its full title is “Impaired Insulin Signaling in Human Adipocytes After Experimental Sleep Restriction: A Randomized, Crossover Study.” ''Scientific American'' says<br />
<br />
<blockquote><br />
For the study, young, healthy, slim subjects spent four nights getting eight and a half hours of sleep and four nights getting only four and a half hours of sleep. The difference in their fat cells was startling: after sleep deprivation, the cells became 30 percent less receptive to insulin signals—a difference that is as large as that between non-diabetic and diabetic patients. <br />
</blockquote><br />
<br />
'''Discussion'''<br />
<br />
1. The ''Scientific American'' article fails to mention the number of subjects: “1 woman, 6 men” or two more than the number of authors of the study. The lone female, “participant 6,” had four of her sixteen data points missing.<br />
<br />
2. The entire study was carried out at one institution. Why might this be a problem?<br />
<br />
3. An extended, positive [http://annals.org/article.aspx?articleID=1379779 editorial commentary in the Annals of Internal Medicine] refers to Aulus Cornelius Celsus who <br />
<br />
<blockquote><br />
argued in favor of “restricted sleep” for the treatment of extra weight…it seems that Celsus may have been wrong: He should have argued in favor of “prolonged sleep” for the treatment of extra weight.<br />
</blockquote><br />
<br />
Look up who Celsus is and why his pronouncements about medical matters might be suspect. Then determine why the commentator claims that the authors “deserve commendation for a study that is a valuable [statistically sound] contribution” to the role of sleep in human health.<br />
<br />
4. As indicated above, each of the subjects were young, healthy and slim. Why is this uniformity good statistically? For inference purposes to a larger population, why is this uniformity not so good statistically?<br />
<br />
Submitted by Paul Alper<br />
<br />
==Sample size criticized==<br />
[http://online.wsj.com/article/SB10001424052970204076204578076891268537914.html?KEYWORDS=timothy+w+martin “Tainted Drug Passed Lab Test”]<br><br />
by Timothy W. Martin <i>et al.</i>, <i>The Wall Street Journal</i>, October 24, 2012<br><br />
<br />
A recent meningitis outbreak (24 dead, 312 sick) was linked to a Massachusetts pharmacy that had produced a steroid product which was tested by an independent lab in Oklahoma. On May 25, based on a test designed to detect fungi, the lab reported that the samples were “sterile” and contained a level of endotoxins that was well below the allowable amount.<br> <br />
<br />
Some experts have criticized the small sample size – 2 five-ml vials out of 6,528.<br><br />
<blockquote>In the case of the … steroids tainted with fungi, the size of the testing sample indicated in the Oklahoma lab report—two vials—is much smaller than the standard for the USP test the lab said it was performing. Under the USP standard, for a batch of more than 6,000 vials, the lab should have tested at least 20.</blockquote><br />
A consultant stated that detection of contamination at a 95% confidence level requires testing of 18% of a batch.<br><br />
<br />
Labs are apparently concerned that the strict testing standards are costly and impractical in some cases. They are calling for looser testing standards.<br />
<blockquote>Only 17 states require that compounding pharmacies follow the U.S. Pharamcopeia guidelines, according to a survey conducted this year by Pharmacy Purchasing & Products, a trade publication.</blockquote><br />
<br />
===Question===<br />
1. In a perfect textbook world, what sample size would you have chosen - out of a population of 6,528 vials of the proposed drug – to test contamination in this case?<br><br />
2. Would you be willing to use a smaller sample if cost had been a factor in the testing? if some patients had had a pressing need for the drug?<br><br />
4. Are there any conditions under which you think it might be appropriate to use a sample size of 2 with respect to testing drugs for contamination?<br> <br />
<br />
Submitted by Margaret Cibes<br />
<br />
==Drivers beware of deer==<br />
[http://www.startribune.com/local/176040491.html 1-in-80 chance of hitting a deer here]<br><br />
''Star Tribune'' (Minneapolis), 27 October 2012<br />
<br />
We read:<br />
<blockquote><br />
Minnesota drivers have a nearly 1-in-80 chance of hitting a deer in the next year, making this the eighth-most likely state for such collisions. Minnesota actually dropped from sixth to eighth in the last year, falling behind Wisconsin, which stayed at No. 7.<br />
<br><br><br />
The statistics come from an analysis prepared by the State Farm insurance company using Federal Highway Administration data.<br />
<br><br><br />
South Dakota moved from third to second on the list with 1-in-68 odds. Iowa dropped from No. 2 to No. 3 with 1-in-71.9 chances. West Virginia was No. 1 with odds of 1 in 40.<br />
</blockquote><br />
<br />
Given the thousands of motorists, can the deer population really be this high?<br />
<br />
'''Discussion'''<br><br />
What do you think this statistic represents? Certainly the ''Washington Post'' interpretation (see Forsooth [http://test.causeweb.org/wiki/chance/index.php/Chance_News_89#Forsooth above]) is not correct.<br />
<br />
Submitted by Paul Alper<br />
<br />
==Skewered charts==<br />
[http://mediamatters.org/research/2012/10/01/a-history-of-dishonest-fox-charts/190225 “A History Of Dishonest Fox Charts”], October 1, 2012<br><br />
<br />
Media Matters has compiled a group of two dozen Fox News charts that showcase a number of potentially exaggerated claims about government activities. The charts contain graphical distortions (y-axes not scaled from 0), as well as content distortion (comparisons of apples to oranges). (Note that all viewers might not agree with the critiques, as witness the heated and personal online blog responses.)<br><br />
<br />
Submitted by Margaret Cibes<br />
<br />
==A forthright stance for uncertainty==<br />
<br />
[http://campaignstops.blogs.nytimes.com/2012/10/30/what-too-close-to-call-really-means/ What Too Close to Call Really Means] by Andrew Gelman, New York Times Campaign Stops blog, October 30, 2012.<br />
<br />
You'll probably end up reading this after the election, but a blog entry seven days before the U.S. presidential election elaborates on why Andrew Gelman believes that the race is too close to call and what that really means.<br />
<br />
First, Dr. Gelman notes how much people want a direct answer to the question "Who is going to win the election on November 6, Barack Obama or Mitt Romney?"<br />
<br />
<blockquote>Different models use different economic and political variables to predict the vote, but these predictions pretty much average to 50-50. People keep wanting me to say that Obama's going to win — I do live near the fabled People's Republic of the Upper West Side, after all — but I keep saying that either side could win. People usually respond to my equivocal non-forecast with a resigned sigh: "I was hoping you’d be able to reassure me."</blockquote><br />
<br />
Different groups provide different probabilities. Nate Silver at the FiveThirtyEight blog at the New York Times placed the probability of an Obama win at 72.9% while the betting service InTrade, places it at 62%. That may sound like a big difference, but <br />
<br />
<blockquote>it corresponds to [http://andrewgelman.com/2012/10/is-it-meaningful-to-talk-about-a-probability-of-65-7-that-obama-will-win-the-election/ something like a difference of half a percentage point] in Obama’s forecast vote share. Put differently, a change in 0.5 percent in the forecast of Obama’s vote share corresponds to a change in a bit more than 10 percent in his probability of winning. Either way, the uncertainty is larger than the best guess at the vote margin.</blockquote><br />
<br />
The whole issue, Dr. Gelman reminds us, is an illustration of how difficult it is to understand probabilities.<br />
<br />
<blockquote> My point is that it’s hard to process probabilities that fall between, say, 60 percent and 90 percent. Less than 60 percent, and I think most people would accept the “too close to call” label. More than 90 percent and you’re ready to start planning for the transition team or your second inaugural ball. In between, though, it’s tough.</blockquote><br />
<br />
Dr. Gelman tries to offer a football analogy. An 80% chance of winning is like being ahead in a (U.S.) football game by three points with five minutes left to play and a 90% chance of winning is like being ahead by seven points with five minutes left to play.<br />
<br />
Lest you accuse Dr. Gelman of ducking the tough calls, he does note a rather bold prediction he made about the U.S. Congressional elections in 2010.<br />
<br />
<blockquote>Let me be clear: I'm not averse to making a strong prediction, when this is warranted by the data. For example, in February 2010, I wrote that "the Democrats are gonna get hammered" in the upcoming congressional elections, as indeed they were. My statement was based on the model of the political scientists Joseph Bafumi, Robert Erikson and Christopher Wlezien, who predicted congressional election voting given generic ballot polling ("If the elections for Congress were being held today, which party’s candidate would you vote for in your Congressional district?"). Their model predicted that the Republicans would win by 8 percentage points (54 percent to 46 percent). That’s the basis of an unambiguous forecast.</blockquote> <br />
<br />
===Discussion===<br />
<br />
1. If Mitt Romney wins on November 6, does that invalidate InTrade's estimate of 62% probability of an Obama win? Does it invalidate Nate Silver's estimate of 72.9% probability of a win?<br />
<br />
2. Does it make sense to report 72.9% versus 73% (or versus 70%) in Nate Silver's model? In other words, how many digits are reliable: three, two, or one?<br />
<br />
3. What is your probability threshold for calling an election "too close to call"?<br />
<br />
Submitted by Steve Simon<br />
<br />
==Randomized trials for parachutes==<br />
For comic relief, Paul Alper sent this spoof from the BMJ archives:<br />
<br />
[http://www.neonatology.org/pdf/ParachuteUseRPCT.pdf Parachute use to prevent death and major trauma related to gravitational challenge: systematic review of randomised controlled trials], by G.C. Smith GC and J.P. Pell, ''BMJ'', 20 December 2003<br />
<br />
Here are some quotations:<br />
<br />
*"The basis for parachute use is purely observational, and its apparent efficacy could potentially be explained by a 'health cohort' effect."<br />
<br />
*"The widespread use of the parachute may just be another example of doctors' obsession with disease prevention and their misplaced belief in unproved technology to provide effective protection against occasional adverse events."<br />
<br />
*"The perception that parachutes are a successful intervention is based largely on anecdotal evidence...We therefore undertook a systematic review of randomised controlled trials of parachutes...Our search strategy did not find any randomised controlled trials of parachutes."<br />
<br />
*"We feel assured that those who advocate evidence based medicine and criticise use of interventions that lack an evidence base will not hesitate to demonstrate their commitment by volunteering for a double blind, randomised placebo controlled, crossover trial."</div>Simon66217https://www.causeweb.org/wiki/chance/index.php?title=Chance_News_89&diff=16608Chance News 892012-10-31T17:38:48Z<p>Simon66217: /* A forthright stance for uncertainty */</p>
<hr />
<div>==Quotations==<br />
"To rephrase Winston Churchill: Polls are the worst form of measuring public opinion — except for all of the others."<br />
<div align=right>--Humphrey Taylor, Chairman of The Harris Poll,<br />
[http://www.nytimes.com/2012/10/25/opinion/political-polls-influential-imperfect-and-everywhere.html Letter to the Editor], ''New York Times'', 24 October 2012 </div><br />
<br />
-----<br />
"We found that almost exactly half of the predictions [by the McLaughlin Group TV pundits in 2008] were right, and almost exactly half were wrong, meaning if you'd just flipped a coin instead of listening to these guys and girls, you would have done just as well. …. One of them, actually … said she thought McCain would win by half a point. Of course, what happened the next week where she came back on the air and said, 'Oh, Obama's win had been inevitable, how could he lose with the economy' ... so there's not really a lot of accountability."<br><br />
<div align=right>Nate Silver in [http://www.npr.org/2012/10/10/162594751/signal-and-noise-prediction-as-art-and-science “‘Signal’ And ‘Noise’: Prediction As Art And Science”], October 10, 2012</div><br />
Submitted by Margaret Cibes<br />
<br />
-----<br />
“The correlation phrase has become so common [on Internet blogs] and so irritating that a minor backlash has now ensued against the rhetoric if not the concept. No, correlation does not imply causation, but it sure as hell provides a hint. …. But there's still another puzzle in the phrase. To say that correlation does not imply causation makes an important point about the limits of statistics, but there are other limits, too, and ones that scientists ignore with far more frequency. In <i>The Cult of Statistical Significance</i>, the economists … cite one of these and make an impassioned, book-length argument against the arbitrary cutoff [5 %] that decides which experimental findings count and which ones don't.”<br><br />
<div align=right>[http://www.slate.com/articles/health_and_science/science/2012/10/correlation_does_not_imply_causation_how_the_internet_fell_in_love_with_a_stats_class_clich_.single.html “The Internet Blowhard’s Favorite Phrase”]<br><br />
by Daniel Engberg, <i>Slate</i>, October 2, 2012</div><br />
Submitted by Margaret Cibes<br />
<br />
==Forsooth==<br />
"Picture yourself behind the wheel on a dark and shadowy night, watching the windshield wipers bat away the rain and wondering, 'What are the odds I’m going to hit a deer?' The answer would be one in 102 if you live in Virginia."<br />
<br />
<div align=right>[http://www.washingtonpost.com/local/trafficandcommuting/odds-of-striking-deer-high-in-maryland-virginia/2012/10/25/69f90f20-1e02-11e2-9cd5-b55c38388962_story.html Odds of striking deer high in Maryland, Virginia], ''Washington Post'', 25 October 2012 </div><br />
<br />
Thanks to Paul Alper for suggesting this story (see more [http://test.causeweb.org/wiki/chance/index.php/Chance_News_89#Drivers_beware_of_deer below]).<br />
<br />
-----<br />
"The main philosophical question is, how should [the recession] be treated? … Should it be treated as an outlier and done away with?"<br />
<br />
<div align=right>Chief statistician at Bureau Economic Analysis<br><br />
quoted by Carl Bialik in [http://online.wsj.com/article/SB10001424052970203897404578078971064492256.html?KEYWORDS=carl+bialik “Economists’ Goal: A Measure for All Seasons]<br><br />
<i>The Wall Street Journal</i>, October 26, 2012</div><br />
Submitted by Margaret Cibes<br />
<br />
==Simpson's paradox on Car Talk==<br />
[http://www.cartalk.com/content/take-ray-out-ball-game?question Take Ray out to the ball game...]<br><br />
Car Talk Puzzler, NPR, 22 September 2012<br />
<br />
Here is the puzzle: Popeye batted .250 for before the All-Star break, while Bluto batted .300; Popeye batted .375 after the All-Star break, while Bluto batted .400. So how did Popeye win his bet that he would have the better average for the season? Statistically minded listeners will quickly recognize this as an instance of Simpson's Paradox. Still, everything sounds like more fun when Tom and Ray discuss it! You can read their solution [http://www.cartalk.com/content/take-ray-out-ball-game?answer here].<br />
<br />
A famous real-life example of Simpson's Paradox with batting averages can be found [http://en.wikipedia.org/wiki/Simpson's_paradox#Batting_averages here].<br />
<br />
==Sleep and fat==<br />
[http://www.scientificamerican.com/podcast/episode.cfm?id=your-fat-needs-sleep-too-12-10-16 Your fat needs sleep, too]<br><br />
by Katherine Harmon, ''Scientific American'', 16 October 2012<br />
<br />
As described in the article (actually the transcript from a "60-Second Health" podcast--you can also listen at the link above):<br />
<blockquote><br />
Sleep is good for you. Getting by on too little sleep increases the risk for heart disease, stroke, high blood pressure, diabetes and other illnesses. It also makes it harder to lose weight or stay slim because sleep deprivation makes you hungrier and less likely to be active during the day.<br />
</blockquote><br />
<br />
Further,<br />
<br />
<blockquote><br />
Now, research shows that sleep also affects fat cells. Our fat cells play an important role in regulating energy use and storage, including insulin processing.<br />
</blockquote><br />
<br />
The research referred to, a randomized crossover study, can be found [http://annals.org/article.aspx?articleid=1379773 in an article] by Josiane Broussard et al. Its full title is “Impaired Insulin Signaling in Human Adipocytes After Experimental Sleep Restriction: A Randomized, Crossover Study.” ''Scientific American'' says<br />
<br />
<blockquote><br />
For the study, young, healthy, slim subjects spent four nights getting eight and a half hours of sleep and four nights getting only four and a half hours of sleep. The difference in their fat cells was startling: after sleep deprivation, the cells became 30 percent less receptive to insulin signals—a difference that is as large as that between non-diabetic and diabetic patients. <br />
</blockquote><br />
<br />
'''Discussion'''<br />
<br />
1. The ''Scientific American'' article fails to mention the number of subjects: “1 woman, 6 men” or two more than the number of authors of the study. The lone female, “participant 6,” had four of her sixteen data points missing.<br />
<br />
2. The entire study was carried out at one institution. Why might this be a problem?<br />
<br />
3. An extended, positive [http://annals.org/article.aspx?articleID=1379779 editorial commentary in the Annals of Internal Medicine] refers to Aulus Cornelius Celsus who <br />
<br />
<blockquote><br />
argued in favor of “restricted sleep” for the treatment of extra weight…it seems that Celsus may have been wrong: He should have argued in favor of “prolonged sleep” for the treatment of extra weight.<br />
</blockquote><br />
<br />
Look up who Celsus is and why his pronouncements about medical matters might be suspect. Then determine why the commentator claims that the authors “deserve commendation for a study that is a valuable [statistically sound] contribution” to the role of sleep in human health.<br />
<br />
4. As indicated above, each of the subjects were young, healthy and slim. Why is this uniformity good statistically? For inference purposes to a larger population, why is this uniformity not so good statistically?<br />
<br />
Submitted by Paul Alper<br />
<br />
==Sample size criticized==<br />
[http://online.wsj.com/article/SB10001424052970204076204578076891268537914.html?KEYWORDS=timothy+w+martin “Tainted Drug Passed Lab Test”]<br><br />
by Timothy W. Martin <i>et al.</i>, <i>The Wall Street Journal</i>, October 24, 2012<br><br />
<br />
A recent meningitis outbreak (24 dead, 312 sick) was linked to a Massachusetts pharmacy that had produced a steroid product which was tested by an independent lab in Oklahoma. On May 25, based on a test designed to detect fungi, the lab reported that the samples were “sterile” and contained a level of endotoxins that was well below the allowable amount.<br> <br />
<br />
Some experts have criticized the small sample size – 2 five-ml vials out of 6,528.<br><br />
<blockquote>In the case of the … steroids tainted with fungi, the size of the testing sample indicated in the Oklahoma lab report—two vials—is much smaller than the standard for the USP test the lab said it was performing. Under the USP standard, for a batch of more than 6,000 vials, the lab should have tested at least 20.</blockquote><br />
A consultant stated that detection of contamination at a 95% confidence level requires testing of 18% of a batch.<br><br />
<br />
Labs are apparently concerned that the strict testing standards are costly and impractical in some cases. They are calling for looser testing standards.<br />
<blockquote>Only 17 states require that compounding pharmacies follow the U.S. Pharamcopeia guidelines, according to a survey conducted this year by Pharmacy Purchasing & Products, a trade publication.</blockquote><br />
<br />
===Question===<br />
1. In a perfect textbook world, what sample size would you have chosen - out of a population of 6,528 vials of the proposed drug – to test contamination in this case?<br><br />
2. Would you be willing to use a smaller sample if cost had been a factor in the testing? if some patients had had a pressing need for the drug?<br><br />
4. Are there any conditions under which you think it might be appropriate to use a sample size of 2 with respect to testing drugs for contamination?<br> <br />
<br />
Submitted by Margaret Cibes<br />
<br />
==Drivers beware of deer==<br />
[http://www.startribune.com/local/176040491.html 1-in-80 chance of hitting a deer here]<br><br />
''Star Tribune'' (Minneapolis), 27 October 2012<br />
<br />
We read:<br />
<blockquote><br />
Minnesota drivers have a nearly 1-in-80 chance of hitting a deer in the next year, making this the eighth-most likely state for such collisions. Minnesota actually dropped from sixth to eighth in the last year, falling behind Wisconsin, which stayed at No. 7.<br />
<br><br><br />
The statistics come from an analysis prepared by the State Farm insurance company using Federal Highway Administration data.<br />
<br><br><br />
South Dakota moved from third to second on the list with 1-in-68 odds. Iowa dropped from No. 2 to No. 3 with 1-in-71.9 chances. West Virginia was No. 1 with odds of 1 in 40.<br />
</blockquote><br />
<br />
Given the thousands of motorists, can the deer population really be this high?<br />
<br />
'''Discussion'''<br><br />
What do you think this statistic represents? Certainly the ''Washington Post'' interpretation (see Forsooth [http://test.causeweb.org/wiki/chance/index.php/Chance_News_89#Forsooth above]) is not correct.<br />
<br />
Submitted by Paul Alper<br />
<br />
==Skewered charts==<br />
[http://mediamatters.org/research/2012/10/01/a-history-of-dishonest-fox-charts/190225 “A History Of Dishonest Fox Charts”], October 1, 2012<br><br />
<br />
Media Matters has compiled a group of two dozen Fox News charts that showcase a number of potentially exaggerated claims about government activities. The charts contain graphical distortions (y-axes not scaled from 0), as well as content distortion (comparisons of apples to oranges). (Note that all viewers might not agree with the critiques, as witness the heated and personal online blog responses.)<br><br />
<br />
Submitted by Margaret Cibes<br />
<br />
==A forthright stance for uncertainty==<br />
<br />
[http://campaignstops.blogs.nytimes.com/2012/10/30/what-too-close-to-call-really-means/ What Too Close to Call Really Means] by Andrew Gelman, New York Times Campaign Stops blog, October 30, 2012.<br />
<br />
You'll probably end up reading this after the election, but a blog entry seven days before the U.S. presidential election elaborates on why Andrew Gelman believes that the race is too close to call and what that really means.<br />
<br />
First, Dr. Gelman notes how much people want a direct answer to the question "Who is going to win the election on November 6, Barack Obama or Mitt Romney?"<br />
<br />
<blockquote>Different models use different economic and political variables to predict the vote, but these predictions pretty much average to 50-50. People keep wanting me to say that Obama's going to win — I do live near the fabled People's Republic of the Upper West Side, after all — but I keep saying that either side could win. People usually respond to my equivocal non-forecast with a resigned sigh: "I was hoping you’d be able to reassure me."</blockquote><br />
<br />
Different groups provide different probabilities. Nate Silver at the FiveThirtyEight blog at the New York Times placed the probability of an Obama win at 72.9% while the betting service InTrade, places it at 62%. That may sound like a big difference, but <br />
<br />
<blockquote>it corresponds to something like a difference of half a percentage point in Obama’s forecast vote share. Put differently, a change in 0.5 percent in the forecast of Obama’s vote share corresponds to a change in a bit more than 10 percent in his probability of winning. Either way, the uncertainty is larger than the best guess at the vote margin.</blockquote><br />
<br />
The whole issue, Dr. Gelman reminds us, is an illustration of how difficult it is to understand probabilities.<br />
<br />
<blockquote> My point is that it’s hard to process probabilities that fall between, say, 60 percent and 90 percent. Less than 60 percent, and I think most people would accept the “too close to call” label. More than 90 percent and you’re ready to start planning for the transition team or your second inaugural ball. In between, though, it’s tough.</blockquote><br />
<br />
Dr. Gelman tries to offer a football analogy. An 80% chance of winning is like being ahead in a (U.S.) football game by three points with five minutes left to play and a 90% chance of winning is like being ahead by seven points with five minutes left to play.<br />
<br />
Lest you accuse Dr. Gelman of ducking the tough calls, he does note a rather bold prediction he made about the U.S. Congressional elections in 2010.<br />
<br />
<blockquote>Let me be clear: I'm not averse to making a strong prediction, when this is warranted by the data. For example, in February 2010, I wrote that "the Democrats are gonna get hammered" in the upcoming congressional elections, as indeed they were. My statement was based on the model of the political scientists Joseph Bafumi, Robert Erikson and Christopher Wlezien, who predicted congressional election voting given generic ballot polling ("If the elections for Congress were being held today, which party’s candidate would you vote for in your Congressional district?"). Their model predicted that the Republicans would win by 8 percentage points (54 percent to 46 percent). That’s the basis of an unambiguous forecast.</blockquote> <br />
<br />
===Discussion===<br />
<br />
1. If Mitt Romney wins on November 6, does that invalidate InTrade's estimate of 62% probability of an Obama win? Does it invalidate Nate Silver's estimate of 72.9% probability of a win?<br />
<br />
2. Does it make sense to report 72.9% versus 73% (or versus 70%) in Nate Silver's model? In other words, how many digits are reliable: three, two, or one?<br />
<br />
3. What is your probability threshold for calling an election "too close to call"?</div>Simon66217https://www.causeweb.org/wiki/chance/index.php?title=Chance_News_89&diff=16607Chance News 892012-10-31T17:32:42Z<p>Simon66217: /* Skewered charts */</p>
<hr />
<div>==Quotations==<br />
"To rephrase Winston Churchill: Polls are the worst form of measuring public opinion — except for all of the others."<br />
<div align=right>--Humphrey Taylor, Chairman of The Harris Poll,<br />
[http://www.nytimes.com/2012/10/25/opinion/political-polls-influential-imperfect-and-everywhere.html Letter to the Editor], ''New York Times'', 24 October 2012 </div><br />
<br />
-----<br />
"We found that almost exactly half of the predictions [by the McLaughlin Group TV pundits in 2008] were right, and almost exactly half were wrong, meaning if you'd just flipped a coin instead of listening to these guys and girls, you would have done just as well. …. One of them, actually … said she thought McCain would win by half a point. Of course, what happened the next week where she came back on the air and said, 'Oh, Obama's win had been inevitable, how could he lose with the economy' ... so there's not really a lot of accountability."<br><br />
<div align=right>Nate Silver in [http://www.npr.org/2012/10/10/162594751/signal-and-noise-prediction-as-art-and-science “‘Signal’ And ‘Noise’: Prediction As Art And Science”], October 10, 2012</div><br />
Submitted by Margaret Cibes<br />
<br />
-----<br />
“The correlation phrase has become so common [on Internet blogs] and so irritating that a minor backlash has now ensued against the rhetoric if not the concept. No, correlation does not imply causation, but it sure as hell provides a hint. …. But there's still another puzzle in the phrase. To say that correlation does not imply causation makes an important point about the limits of statistics, but there are other limits, too, and ones that scientists ignore with far more frequency. In <i>The Cult of Statistical Significance</i>, the economists … cite one of these and make an impassioned, book-length argument against the arbitrary cutoff [5 %] that decides which experimental findings count and which ones don't.”<br><br />
<div align=right>[http://www.slate.com/articles/health_and_science/science/2012/10/correlation_does_not_imply_causation_how_the_internet_fell_in_love_with_a_stats_class_clich_.single.html “The Internet Blowhard’s Favorite Phrase”]<br><br />
by Daniel Engberg, <i>Slate</i>, October 2, 2012</div><br />
Submitted by Margaret Cibes<br />
<br />
==Forsooth==<br />
"Picture yourself behind the wheel on a dark and shadowy night, watching the windshield wipers bat away the rain and wondering, 'What are the odds I’m going to hit a deer?' The answer would be one in 102 if you live in Virginia."<br />
<br />
<div align=right>[http://www.washingtonpost.com/local/trafficandcommuting/odds-of-striking-deer-high-in-maryland-virginia/2012/10/25/69f90f20-1e02-11e2-9cd5-b55c38388962_story.html Odds of striking deer high in Maryland, Virginia], ''Washington Post'', 25 October 2012 </div><br />
<br />
Thanks to Paul Alper for suggesting this story (see more [http://test.causeweb.org/wiki/chance/index.php/Chance_News_89#Drivers_beware_of_deer below]).<br />
<br />
-----<br />
"The main philosophical question is, how should [the recession] be treated? … Should it be treated as an outlier and done away with?"<br />
<br />
<div align=right>Chief statistician at Bureau Economic Analysis<br><br />
quoted by Carl Bialik in [http://online.wsj.com/article/SB10001424052970203897404578078971064492256.html?KEYWORDS=carl+bialik “Economists’ Goal: A Measure for All Seasons]<br><br />
<i>The Wall Street Journal</i>, October 26, 2012</div><br />
Submitted by Margaret Cibes<br />
<br />
==Simpson's paradox on Car Talk==<br />
[http://www.cartalk.com/content/take-ray-out-ball-game?question Take Ray out to the ball game...]<br><br />
Car Talk Puzzler, NPR, 22 September 2012<br />
<br />
Here is the puzzle: Popeye batted .250 for before the All-Star break, while Bluto batted .300; Popeye batted .375 after the All-Star break, while Bluto batted .400. So how did Popeye win his bet that he would have the better average for the season? Statistically minded listeners will quickly recognize this as an instance of Simpson's Paradox. Still, everything sounds like more fun when Tom and Ray discuss it! You can read their solution [http://www.cartalk.com/content/take-ray-out-ball-game?answer here].<br />
<br />
A famous real-life example of Simpson's Paradox with batting averages can be found [http://en.wikipedia.org/wiki/Simpson's_paradox#Batting_averages here].<br />
<br />
==Sleep and fat==<br />
[http://www.scientificamerican.com/podcast/episode.cfm?id=your-fat-needs-sleep-too-12-10-16 Your fat needs sleep, too]<br><br />
by Katherine Harmon, ''Scientific American'', 16 October 2012<br />
<br />
As described in the article (actually the transcript from a "60-Second Health" podcast--you can also listen at the link above):<br />
<blockquote><br />
Sleep is good for you. Getting by on too little sleep increases the risk for heart disease, stroke, high blood pressure, diabetes and other illnesses. It also makes it harder to lose weight or stay slim because sleep deprivation makes you hungrier and less likely to be active during the day.<br />
</blockquote><br />
<br />
Further,<br />
<br />
<blockquote><br />
Now, research shows that sleep also affects fat cells. Our fat cells play an important role in regulating energy use and storage, including insulin processing.<br />
</blockquote><br />
<br />
The research referred to, a randomized crossover study, can be found [http://annals.org/article.aspx?articleid=1379773 in an article] by Josiane Broussard et al. Its full title is “Impaired Insulin Signaling in Human Adipocytes After Experimental Sleep Restriction: A Randomized, Crossover Study.” ''Scientific American'' says<br />
<br />
<blockquote><br />
For the study, young, healthy, slim subjects spent four nights getting eight and a half hours of sleep and four nights getting only four and a half hours of sleep. The difference in their fat cells was startling: after sleep deprivation, the cells became 30 percent less receptive to insulin signals—a difference that is as large as that between non-diabetic and diabetic patients. <br />
</blockquote><br />
<br />
'''Discussion'''<br />
<br />
1. The ''Scientific American'' article fails to mention the number of subjects: “1 woman, 6 men” or two more than the number of authors of the study. The lone female, “participant 6,” had four of her sixteen data points missing.<br />
<br />
2. The entire study was carried out at one institution. Why might this be a problem?<br />
<br />
3. An extended, positive [http://annals.org/article.aspx?articleID=1379779 editorial commentary in the Annals of Internal Medicine] refers to Aulus Cornelius Celsus who <br />
<br />
<blockquote><br />
argued in favor of “restricted sleep” for the treatment of extra weight…it seems that Celsus may have been wrong: He should have argued in favor of “prolonged sleep” for the treatment of extra weight.<br />
</blockquote><br />
<br />
Look up who Celsus is and why his pronouncements about medical matters might be suspect. Then determine why the commentator claims that the authors “deserve commendation for a study that is a valuable [statistically sound] contribution” to the role of sleep in human health.<br />
<br />
4. As indicated above, each of the subjects were young, healthy and slim. Why is this uniformity good statistically? For inference purposes to a larger population, why is this uniformity not so good statistically?<br />
<br />
Submitted by Paul Alper<br />
<br />
==Sample size criticized==<br />
[http://online.wsj.com/article/SB10001424052970204076204578076891268537914.html?KEYWORDS=timothy+w+martin “Tainted Drug Passed Lab Test”]<br><br />
by Timothy W. Martin <i>et al.</i>, <i>The Wall Street Journal</i>, October 24, 2012<br><br />
<br />
A recent meningitis outbreak (24 dead, 312 sick) was linked to a Massachusetts pharmacy that had produced a steroid product which was tested by an independent lab in Oklahoma. On May 25, based on a test designed to detect fungi, the lab reported that the samples were “sterile” and contained a level of endotoxins that was well below the allowable amount.<br> <br />
<br />
Some experts have criticized the small sample size – 2 five-ml vials out of 6,528.<br><br />
<blockquote>In the case of the … steroids tainted with fungi, the size of the testing sample indicated in the Oklahoma lab report—two vials—is much smaller than the standard for the USP test the lab said it was performing. Under the USP standard, for a batch of more than 6,000 vials, the lab should have tested at least 20.</blockquote><br />
A consultant stated that detection of contamination at a 95% confidence level requires testing of 18% of a batch.<br><br />
<br />
Labs are apparently concerned that the strict testing standards are costly and impractical in some cases. They are calling for looser testing standards.<br />
<blockquote>Only 17 states require that compounding pharmacies follow the U.S. Pharamcopeia guidelines, according to a survey conducted this year by Pharmacy Purchasing & Products, a trade publication.</blockquote><br />
<br />
===Question===<br />
1. In a perfect textbook world, what sample size would you have chosen - out of a population of 6,528 vials of the proposed drug – to test contamination in this case?<br><br />
2. Would you be willing to use a smaller sample if cost had been a factor in the testing? if some patients had had a pressing need for the drug?<br><br />
4. Are there any conditions under which you think it might be appropriate to use a sample size of 2 with respect to testing drugs for contamination?<br> <br />
<br />
Submitted by Margaret Cibes<br />
<br />
==Drivers beware of deer==<br />
[http://www.startribune.com/local/176040491.html 1-in-80 chance of hitting a deer here]<br><br />
''Star Tribune'' (Minneapolis), 27 October 2012<br />
<br />
We read:<br />
<blockquote><br />
Minnesota drivers have a nearly 1-in-80 chance of hitting a deer in the next year, making this the eighth-most likely state for such collisions. Minnesota actually dropped from sixth to eighth in the last year, falling behind Wisconsin, which stayed at No. 7.<br />
<br><br><br />
The statistics come from an analysis prepared by the State Farm insurance company using Federal Highway Administration data.<br />
<br><br><br />
South Dakota moved from third to second on the list with 1-in-68 odds. Iowa dropped from No. 2 to No. 3 with 1-in-71.9 chances. West Virginia was No. 1 with odds of 1 in 40.<br />
</blockquote><br />
<br />
Given the thousands of motorists, can the deer population really be this high?<br />
<br />
'''Discussion'''<br><br />
What do you think this statistic represents? Certainly the ''Washington Post'' interpretation (see Forsooth [http://test.causeweb.org/wiki/chance/index.php/Chance_News_89#Forsooth above]) is not correct.<br />
<br />
Submitted by Paul Alper<br />
<br />
==Skewered charts==<br />
[http://mediamatters.org/research/2012/10/01/a-history-of-dishonest-fox-charts/190225 “A History Of Dishonest Fox Charts”], October 1, 2012<br><br />
<br />
Media Matters has compiled a group of two dozen Fox News charts that showcase a number of potentially exaggerated claims about government activities. The charts contain graphical distortions (y-axes not scaled from 0), as well as content distortion (comparisons of apples to oranges). (Note that all viewers might not agree with the critiques, as witness the heated and personal online blog responses.)<br><br />
<br />
Submitted by Margaret Cibes<br />
<br />
==A forthright stance for uncertainty==<br />
<br />
[http://campaignstops.blogs.nytimes.com/2012/10/30/what-too-close-to-call-really-means/ What Too Close to Call Really Means] by Andrew Gelman, New York times Campaign Stops blog, October 30, 2012.<br />
<br />
You'll probably end up reading this after the election, but a blog entry seven days before the U.S. presidential election elaborates on why Andrew Gelman believes that the race is too close to call and what that really means.<br />
<br />
First, Dr. Gelman notes how much people want a direct answer to the question "Who is going to win the election on November 6, Barack Obama or Mitt Romney.<br />
<br />
<blockquote>Different models use different economic and political variables to predict the vote, but these predictions pretty much average to 50-50. People keep wanting me to say that Obama's going to win — I do live near the fabled People's Republic of the Upper West Side, after all — but I keep saying that either side could win. People usually respond to my equivocal non-forecast with a resigned sigh: "I was hoping you’d be able to reassure me."</blockquote><br />
<br />
Different groups provide different probabilities. Nate Silver at the FiveThirtyEight blog at the New York Times placed the probability of an Obama win at 72.9% while the betting service InTrade, places it at 62%. That may sound like a big difference, but <br />
<br />
<blockquote>it corresponds to something like a difference of half a percentage point in Obama’s forecast vote share. Put differently, a change in 0.5 percent in the forecast of Obama’s vote share corresponds to a change in a bit more than 10 percent in his probability of winning. Either way, the uncertainty is larger than the best guess at the vote margin.</blockquote><br />
<br />
The whole issue, Dr. Gelman reminds us, is an illustration of how difficult it is to understand probabilities.<br />
<br />
<blockquote> My point is that it’s hard to process probabilities that fall between, say, 60 percent and 90 percent. Less than 60 percent, and I think most people would accept the “too close to call” label. More than 90 percent and you’re ready to start planning for the transition team or your second inaugural ball. In between, though, it’s tough.</blockquote><br />
<br />
Mr. Gleman tries to offer a football analogy. An 80% chance of winning is like being ahead in a (U.S.) football game by three points with five minutes left to play and a 90% chance of winning is like being ahead by seven points with five minutes left to play.<br />
<br />
Lest you accuse Dr. Gleman of ducking the tough calls, he does note a rather bold prediction he made about the U.S. Congressional elections in 2010.<br />
<br />
<blockquote>Let me be clear: I’m not averse to making a strong prediction, when this is warranted by the data. For example, in Feburary 2010, I wrote that “the Democrats are gonna get hammered” in the upcoming congressional elections, as indeed they were. My statement was based on the model of the political scientists Joseph Bafumi, Robert Erikson and Christopher Wlezien, who predicted congressional election voting given generic ballot polling (“If the elections for Congress were being held today, which party’s candidate would you vote for in your Congressional district?”). Their model predicted that the Republicans would win by 8 percentage points (54 percent to 46 percent). That’s the basis of an unambiguous forecast.</blockquote> <br />
<br />
===Discussion===<br />
<br />
1. If Mitt Romney wins on November 6, does that invalidate InTrade's estimate of 62% probability of an Obama win? Does it invalidate Nate Silver's estimate of 72.9% probability of a win?<br />
<br />
2. Does it make sense to report 72.9% versus 73% in Nate Silver's model?<br />
<br />
3. What is your probability threshold for calling an election "too close to call"?</div>Simon66217https://www.causeweb.org/wiki/chance/index.php?title=Chance_News_88&diff=16491Chance News 882012-10-08T20:32:48Z<p>Simon66217: /* Is someone cooking the unemployment numbers */</p>
<hr />
<div>==Quotations==<br />
"If you are a passenger on a plane and the pilot tells you he has a faulty map, you get off the plane; you don’t stay and say 'well, there is nothing better.' But in economics, particularly finance, they keep teaching these models on grounds that 'there is nothing better,' causing harmful risk-taking."<br />
<br />
<div align=right>--Nassim Nicholas Taleb, in [http://www.nytimes.com/roomfordebate/2012/04/01/how-to-teach-economics-after-the-financial-crisis/throw-out-the-old-economic-models Throw out the probability models], ''New York Times'' Room for Debate, 2 April 2012</div><br />
<br />
==Forsooth==<br />
“Odds of becoming a top ranked NASCAR driver: 1 in 125 billion.”<br />
<div align=right>from an advertisement by Autism Speaks in ''Sports Illustrated''</div><br />
<br />
(There are only about 7 billion people in the world, so if there are only two “top ranked drivers” then the odds are only 1 in 3.5 billion or so.)<br />
<br />
Submitted by Marc Hurwitz<br />
<br />
==Impact and retract==<br />
<br />
As unlikely as it may seem, there are many thousands (!) of health/medical journals published each month. Obviously, some carry more clout than others when it comes to promotion and reputation of contributing authors. Those journals are said to have high “impact factors.” The de facto and default definition of IF, according to [http://en.wikipedia.org/wiki/Impact_factor Wikipedia] “was devised by Eugene Garfield, the founder of the Institute for Scientific Information (ISI), now part of Thomson Reuters. Impact factors are calculated yearly for those journals that are indexed in Thomson Reuters Journal Citation Reports.”<br />
<br />
The calculation of IF is a bit involved:<br />
<br />
<blockquote>In a given year, the impact factor of a journal is the average number of citations received per paper published in that journal during the two preceding years. For example, if a journal has an impact factor of 3 in 2008, then its papers published in 2006 and 2007 received 3 citations each on average in 2008. The 2008 impact factor of a journal would be calculated as follows:<br />
:''A'' = the number of times articles published in 2006 and 2007 were cited by indexed journals during 2008.<br />
:''B'' = the total number of "citable items" published by that journal in 2006 and 2007. ("Citable items" are usually articles, reviews, proceedings, or notes; not editorials or Letters-to-the-Editor.)<br />
:2008 impact factor = ''A/B''.<br />
(Note that 2008 impact factors are actually published in 2009; they cannot be calculated until all of the 2008 publications have been processed by the indexing agency.)</blockquote><br />
<br />
Of course, when there is an “''A'' over ''B''” you can be sure that some journals might be tempted to inflate ''A'' and/or lower ''B'' to obtain a higher IF.<br />
<br />
<blockquote>A journal can adopt editorial policies that increase its impact factor. For example, journals may publish a larger percentage of review articles which generally are cited more than research reports. Therefore review articles can raise the impact factor of the journal and review journals will therefore often have the highest impact factors in their respective fields. Journals may also attempt to limit the number of "citable items", ie the denominator of the IF equation, either by declining to publish articles (such as case reports in medical journals) which are unlikely to be cited or by altering articles (by not allowing an abstract or bibliography) in hopes that Thomson Scientific will not deem it a "citable item". (As a result of negotiations over whether items are "citable", impact factor variations of more than 300% have been observed.) </blockquote><br />
<br />
Then, there is “coercive citation” <br />
<br />
<blockquote>in which an editor forces an author to add spurious self-citations to an article before the journal will agree to publish it in order to inflate the journal's impact factor. </blockquote><br />
<br />
The pressure on a researcher to publish in high IF journals according to <br />
[http://blogs.lse.ac.uk/impactofsocialsciences/2011/12/19/impact-factor-citations-retractions/ Björn Brembs] is extremely high:<br />
<br />
<blockquote>As a scientist today, it is very difficult to find employment if you cannot sport publications in high-ranking journals. In the increasing competition for the coveted spots, it is starting to be difficult to find employment with only few papers in high-ranking journals: a consistent record of ‘high-impact’ publications is required if you want science to be able to put food on your table. Subjective impressions appear to support this intuitive notion: isn’t a lot of great research published in Science and Nature while we so often find horrible work published in little-known journals? Isn’t it a good thing that in times of shrinking budgets we only allow the very best scientists to continue spending taxpayer funds? </blockquote><br />
<br />
Ah, but Brembs then points out that as plausible as the above argument is regarding the superiority of high IF journals, the data do not support that statement. He refers to an article by [http://iai.asm.org/content/79/10/3855.full?maxtoshow=&hits=10&RESULTFORMAT=&fulltext=%25DF&searchid=1&FIRSTINDEX=2663&resourcetype=HWFIG Fang and Casadevall] from which he obtains this stunning regression graph:<br />
<br />
<center> [[File:Brembs.png]] </center><br />
<br />
The retraction index is the number of retractions in the journal from 2001 to 2010, multiplied by 1000, and divided by the number of published articles with abstracts. The p-value for slope is exceedingly small and the coefficient of determination is .77. Thus, “at least with the current data, IF indeed seems to be a more reliable predictor of retractions than of actual citations.” He reasons that<br />
<br />
<blockquote>If your livelihood depends on this Science/Nature paper, doesn’t the pressure increase to maybe forget this one crucial control experiment, or leave out some data points that don’t quite make the story look so nice? After all, you know your results are solid, it’s only cosmetics which are required to make it a top-notch publication! Of course, in science there never is certainty, so such behavior will decrease the reliability of the scientific reports being published. And indeed, together with the decrease in tenured positions, the number of retractions has increased at about 400-fold the rate of publication increase. </blockquote><br />
<br />
===Discussion===<br />
<br />
1. Obtain a (very) good dictionary to see how the grammatical uses of the word “impact” has differed down through the centuries with a shift taking place somewhere in the post-World-War-II world. Ask an elderly person for his view of “impact” as a verb let alone as an adjective. Do the same for the word “contact” which had a grammatical shift in the 1920s.<br />
<br />
2. The Fang and Casadevall paper had the graph presented this way:<br />
<br />
<center> [[File:FangCasadevallFig1.png]] </center><br />
<br />
Why is Brembs’ version more suggestive of a cause (IF) and effect (retraction index) relationship?<br />
<br />
3. Give a plausibility argument for why many low-level IF journals might have a virtually zero retraction index.<br />
<br />
4. For an exceedingly interesting interview with Fang and Casadevall see [http://www.nytimes.com/2012/04/17/science/rise-in-scientific-journal-retractions-prompts-calls-for-reform.html?pagewanted=all Carl Zimmer’s NYT article.] <br />
<blockquote><br />
Several factors are at play here, scientists say. One may be that because journals are now online, bad papers are simply reaching a wider audience, making it more likely that errors will be spotted. “You can sit at your laptop and pull a lot of different papers together,” Dr. Fang said.<br><br><br />
But other forces are more pernicious. To survive professionally, scientists feel the need to publish as many papers as possible, and to get them into high-profile journals. And sometimes they cut corners or even commit misconduct to get there.<br><br><br />
Each year, every laboratory produces a new crop of Ph.D.’s, who must compete for a small number of jobs, and the competition is getting fiercer. In 1973, more than half of biologists had a tenure-track job within six years of getting a Ph.D. By 2006 the figure was down to 15 percent. </blockquote><br />
The article is packed with intriguing discussion points about funding and ends with Fang’s pessimistic/realistic lament:<br />
<blockquote> “When our generation goes away, where is the new generation going to be?” he asked. “All the scientists I know are so anxious about their funding that they don’t make inspiring role models. I heard it from my own kids, who went into art and music respectively. They said, ‘You know, we see you, and you don’t look very happy.’ ” </blockquote><br />
<br />
Submitted by Paul Alper<br />
<br />
==Poker: Skill or luck?==<br />
[http://www.nytimes.com/2012/08/22/nyregion/poker-is-more-a-game-of-skill-than-of-chance-a-judge-rules.html?ref=opinion Poker is more a game of skill than of chance, a judge rules]<br><br />
by Mosi Secret, ''New York Times'', 21 August 2012<br />
<br />
[http://www.nytimes.com/2012/08/25/opinion/poker-an-american-pastime-and-a-game-of-skill.html?scp=1&sq=poker&st=Search No more bluffing]<br><br />
by James McManus, ''New York Times'', 24 August 2012<br />
<br />
This is not just a parlor debate. If poker is a game of skill rather than chance, then it cannot be regulated by laws governing gambling activity. See [http://test.causeweb.org/wiki/chance/index.php?title=Chance_News_46&action=edit&section=8 Chance News 46] for an earlier discussion of this topic.<br />
<br />
To be continued...<br />
<br />
Submitted by Bill Peterson<br />
<br />
==Where are the 47%?==<br />
[http://www.theatlanticcities.com/jobs-and-economy/2012/09/geography-47/3323/ The geography of the 47%]<br><br />
by Richard Florida, TheAtlanticCities.com, 19 September 2012<br />
<br />
The article included the scatterplot shown below. Each point represents a state. The full version from the Atlantic (available [http://charts.theatlantic.com/embed/505a126cbf96c4168d000035/ here]) is interactive: you can click on <br />
points to identify the state.<br />
:[[File:Nonpayers.png]]<br />
<br />
<br />
The [http://taxfoundation.org/article/nonpayers-state-2010 tax data] are available from the TaxFoundation.org. Note that non-payers<br />
are defined as those who filed tax returns indicating no liability. As explained in the article, there are other nonpayers who are not required to file (which is why there are no points on the plot at 47% or more!).<br />
<br />
Suggested by Margaret Cibes<br />
<br />
==The sexiest job==<br />
[http://hbr.org/2012/10/data-scientist-the-sexiest-job-of-the-21st-century/ Data scientist: The sexiest job of the 21st century]<br><br />
by Thomas H. Davenport and D.J. Patil , ''Harvard Business Review'', October 2012<br />
<br />
According to the article, the job title ''data scientist'' was "coined in 2008 by one of us, D.J. Patil, and Jeff Hammerbacher, then the respective leads of data and analytics efforts at LinkedIn and Facebook." <br />
<br />
Thanks to Nick Horton, who sent this link to the Isolated Statisticians list.<br />
<br />
==Skewed polling==<br />
[http://www.examiner.com/article/the-skewed-polls-issue-and-why-it-is-important-1 The skewed polls issue and why it is important]<br><br />
by Dean Chambers, Examiner.com, 25 September 2012<br />
<br />
Chambers has a website, [http://unskewedpolls.com UnskewedPolls], where he reanalyzes polls published by other organizations in order to adjust for <br />
what he sees as inherent bias. A number of recent polls have shown President Obama with a lead in key swing states. Chambers challenges these results on the basis that respondents who self-identify as Democrats comprise too large a proportion of the sample. By reweighting the results to reflect what he asserts are the true <br />
party proportions among all voters, Chambers finds that most polling data actually indicate that Romney is leading. Here is one example from the article<br />
<blockquote><br />
The Gallup tracking poll, which has been over-sampled Democrats in the past, has released its latest numbers today showing President Obama leading 48 percent to 45 percent for Mitt Romney. But the non-skewed uses a sample weighted by the expected partisan makeup of the electorate, the QStarNews Daily Tracking poll [Chambers's<br />
organization], shows Romney leading over Obama by a 53 percent to 45 percent margin. <br />
</blockquote><br />
Gallup's editor-in-chief, Frank Newport, responds to this issue in a recent post, [http://pollingmatters.gallup.com/2012/09/the-recurring-and-misleading-focus-on.html The recurring -- and misleading -- focus on party Identification] (27 September 2012). He says that Gallup determines party identification as part of its surveys, <br />
asking, “In politics, as of today, do you consider yourself a Republican, a Democrat, or an independent?" Thus, rather than reflecting fixed percentages, party affiliation is itself dynamic. In other words, what Chambers interprets as an over-sampling of Democrats may instead reflect increasing support for the Democratic candidate.<br />
<br />
==Tonight Show birthday problem==<br />
[http://opinionator.blogs.nytimes.com/2012/10/01/its-my-birthday-too-yeah/?hp It’s my birthday too, yeah]<br><br />
by Steven Strogatz, ''New York Times'', 1 October 2012<br />
<br />
We were very happy to learn that Steven Stogatz has returned to the ''Times'' with a new Opinionator series entitled <br />
[http://opinionator.blogs.nytimes.com/category/me-myself-and-math/ Me, Myself and Math] (his earlier series, [http://topics.nytimes.com/top/opinion/series/steven_strogatz_on_the_elements_of_math/index.html The Elements of Math], appeared in 2010).<br />
<br />
For the present piece, he has unearthed some wonderful [http://www.cornell.edu/video/?videoid=2334 archival video] of a Tonight Show episode from 1980, in which Johnny Carson<br />
and Ed McMahon attempt to validate the famous birthday problem probability using the studio audience. Alas, Ed inadvertently leads Johnny to confuse this with the "birthmate problem" (how<br />
many people do you need in a room to have a better than even chance of matching ''your'' birthday?). They wind up asking for the birthday of an audience member <br />
seated in the front row, and are then (comically) puzzled when no one else shares that birthday. But do watch the video--a verbal description doesn't do justice to Johnny's inimitable style.<br />
<br />
The surprising new revelation is that it was Carson himself who brought up the birthday problem! As described in the article, various retellings of the story over the years have inserted a guest mathematician/statistician whose attempt to explain the problem was derailed by the host. <br />
<br />
The notes at the end of the article provide some great pointers to further discussion and applications of the problem. <br />
<br />
Submitted by Bill Peterson<br />
<br />
==Correlation does not imply causation==<br />
Jeff Witmer posted this link to the Isolated Statisticians mailing list:<br />
<br />
[http://www.slate.com/articles/health_and_science/science/2012/10/correlation_does_not_imply_causation_how_the_internet_fell_in_love_with_a_stats_class_clich_.single.html The Internet blowhard’s favorite phrase]<br><br />
by Daniel Engber, ''Slate'', 2 October 2012<br />
<br />
This essay--subtitled "Why do people love to say that correlation does not imply causation?"--explores the origin of the phrase, and some pushback against its reflexive use in online debates.<br />
<br />
==Is someone cooking the unemployment numbers==<br />
<br />
[http://economix.blogs.nytimes.com/2012/10/05/explaining-the-big-gain-in-job-getters/ Taming volatile Raw Data for Jobs Reports], Catherine Rampbell, The New York Times, October 5, 2012.<br />
<br />
The most recent unemployment data is good, with a reported unemployment rate below 8%. That has some people upset. Many conservatives (e.g., [http://economix.blogs.nytimes.com/2012/10/05/from-jack-welch-a-conspiracy-theory/ Jack Welch], [http://www.huffingtonpost.com/2012/10/05/joe-scarborough-jobs-report-numbers_n_1942430.html Joe Scarborough], [http://newsbusters.org/blogs/mark-finkelstein/2012/10/05/santelli-smells-rat-i-told-you-theyd-get-it-under-8-they-did Rick Santelli]) expressed a concern that the Bureau of Labor Statistics (BLS) may have cheated in order to help get President Obama re-elected.<br />
<br />
These criticisms ignore the fact that all of the statisticians at BLS are not political appointees, but career civil servants who have a fair amount of insulation from political pressures. Catherine Rampbell points out some issues with unemployment figures that may lead to confusion.<br />
<br />
<blockquote>These numbers are always tremendously volatile, but the reasons are statistical, not political. The numbers come from a tiny survey with a margin of error of 400,000. Every month there are wild swings, and no one takes them at face value. The swings usually attract less attention, though, because the political stakes are usually lower.</blockquote><br />
<br />
Another issue is the use of seasonal adjustments. Unemployment rates do have predictable shifts based on the calendar. In particular, there is a large change in employment as younger workers leave their summer jobs and go back to college.<br />
<br />
<blockquote>The Bureau of Labor Statistics adjusts its raw survey data to correct for seasonal patterns, and since a decline in employment is expected for those 20 to 24, the economists at the bureau increased the level of employment for this group in the seasonally adjusted numbers.</blockquote><br />
<br />
It's possible that the seasonal adjustment was an overadjustment.<br />
<br />
<blockquote>Changes in seasonal patterns like this one can introduce more error into the headline numbers, and can at least partly explain why the overall change in household employment looked so much bigger in September than seems plausible.</blockquote><br />
<br />
Submitted by Steve Simon<br />
<br />
===Discussion===<br />
<br />
1. How can the BLS de-politicize its unemployment report?<br />
<br />
2. How can the media de-politicize the unemployment report?<br />
<br />
3. How well do you think the civil service system protects career civil servants from political pressures?</div>Simon66217https://www.causeweb.org/wiki/chance/index.php?title=Chance_News_88&diff=16490Chance News 882012-10-08T20:32:23Z<p>Simon66217: /* Questions */</p>
<hr />
<div>==Quotations==<br />
"If you are a passenger on a plane and the pilot tells you he has a faulty map, you get off the plane; you don’t stay and say 'well, there is nothing better.' But in economics, particularly finance, they keep teaching these models on grounds that 'there is nothing better,' causing harmful risk-taking."<br />
<br />
<div align=right>--Nassim Nicholas Taleb, in [http://www.nytimes.com/roomfordebate/2012/04/01/how-to-teach-economics-after-the-financial-crisis/throw-out-the-old-economic-models Throw out the probability models], ''New York Times'' Room for Debate, 2 April 2012</div><br />
<br />
==Forsooth==<br />
“Odds of becoming a top ranked NASCAR driver: 1 in 125 billion.”<br />
<div align=right>from an advertisement by Autism Speaks in ''Sports Illustrated''</div><br />
<br />
(There are only about 7 billion people in the world, so if there are only two “top ranked drivers” then the odds are only 1 in 3.5 billion or so.)<br />
<br />
Submitted by Marc Hurwitz<br />
<br />
==Impact and retract==<br />
<br />
As unlikely as it may seem, there are many thousands (!) of health/medical journals published each month. Obviously, some carry more clout than others when it comes to promotion and reputation of contributing authors. Those journals are said to have high “impact factors.” The de facto and default definition of IF, according to [http://en.wikipedia.org/wiki/Impact_factor Wikipedia] “was devised by Eugene Garfield, the founder of the Institute for Scientific Information (ISI), now part of Thomson Reuters. Impact factors are calculated yearly for those journals that are indexed in Thomson Reuters Journal Citation Reports.”<br />
<br />
The calculation of IF is a bit involved:<br />
<br />
<blockquote>In a given year, the impact factor of a journal is the average number of citations received per paper published in that journal during the two preceding years. For example, if a journal has an impact factor of 3 in 2008, then its papers published in 2006 and 2007 received 3 citations each on average in 2008. The 2008 impact factor of a journal would be calculated as follows:<br />
:''A'' = the number of times articles published in 2006 and 2007 were cited by indexed journals during 2008.<br />
:''B'' = the total number of "citable items" published by that journal in 2006 and 2007. ("Citable items" are usually articles, reviews, proceedings, or notes; not editorials or Letters-to-the-Editor.)<br />
:2008 impact factor = ''A/B''.<br />
(Note that 2008 impact factors are actually published in 2009; they cannot be calculated until all of the 2008 publications have been processed by the indexing agency.)</blockquote><br />
<br />
Of course, when there is an “''A'' over ''B''” you can be sure that some journals might be tempted to inflate ''A'' and/or lower ''B'' to obtain a higher IF.<br />
<br />
<blockquote>A journal can adopt editorial policies that increase its impact factor. For example, journals may publish a larger percentage of review articles which generally are cited more than research reports. Therefore review articles can raise the impact factor of the journal and review journals will therefore often have the highest impact factors in their respective fields. Journals may also attempt to limit the number of "citable items", ie the denominator of the IF equation, either by declining to publish articles (such as case reports in medical journals) which are unlikely to be cited or by altering articles (by not allowing an abstract or bibliography) in hopes that Thomson Scientific will not deem it a "citable item". (As a result of negotiations over whether items are "citable", impact factor variations of more than 300% have been observed.) </blockquote><br />
<br />
Then, there is “coercive citation” <br />
<br />
<blockquote>in which an editor forces an author to add spurious self-citations to an article before the journal will agree to publish it in order to inflate the journal's impact factor. </blockquote><br />
<br />
The pressure on a researcher to publish in high IF journals according to <br />
[http://blogs.lse.ac.uk/impactofsocialsciences/2011/12/19/impact-factor-citations-retractions/ Björn Brembs] is extremely high:<br />
<br />
<blockquote>As a scientist today, it is very difficult to find employment if you cannot sport publications in high-ranking journals. In the increasing competition for the coveted spots, it is starting to be difficult to find employment with only few papers in high-ranking journals: a consistent record of ‘high-impact’ publications is required if you want science to be able to put food on your table. Subjective impressions appear to support this intuitive notion: isn’t a lot of great research published in Science and Nature while we so often find horrible work published in little-known journals? Isn’t it a good thing that in times of shrinking budgets we only allow the very best scientists to continue spending taxpayer funds? </blockquote><br />
<br />
Ah, but Brembs then points out that as plausible as the above argument is regarding the superiority of high IF journals, the data do not support that statement. He refers to an article by [http://iai.asm.org/content/79/10/3855.full?maxtoshow=&hits=10&RESULTFORMAT=&fulltext=%25DF&searchid=1&FIRSTINDEX=2663&resourcetype=HWFIG Fang and Casadevall] from which he obtains this stunning regression graph:<br />
<br />
<center> [[File:Brembs.png]] </center><br />
<br />
The retraction index is the number of retractions in the journal from 2001 to 2010, multiplied by 1000, and divided by the number of published articles with abstracts. The p-value for slope is exceedingly small and the coefficient of determination is .77. Thus, “at least with the current data, IF indeed seems to be a more reliable predictor of retractions than of actual citations.” He reasons that<br />
<br />
<blockquote>If your livelihood depends on this Science/Nature paper, doesn’t the pressure increase to maybe forget this one crucial control experiment, or leave out some data points that don’t quite make the story look so nice? After all, you know your results are solid, it’s only cosmetics which are required to make it a top-notch publication! Of course, in science there never is certainty, so such behavior will decrease the reliability of the scientific reports being published. And indeed, together with the decrease in tenured positions, the number of retractions has increased at about 400-fold the rate of publication increase. </blockquote><br />
<br />
===Discussion===<br />
<br />
1. Obtain a (very) good dictionary to see how the grammatical uses of the word “impact” has differed down through the centuries with a shift taking place somewhere in the post-World-War-II world. Ask an elderly person for his view of “impact” as a verb let alone as an adjective. Do the same for the word “contact” which had a grammatical shift in the 1920s.<br />
<br />
2. The Fang and Casadevall paper had the graph presented this way:<br />
<br />
<center> [[File:FangCasadevallFig1.png]] </center><br />
<br />
Why is Brembs’ version more suggestive of a cause (IF) and effect (retraction index) relationship?<br />
<br />
3. Give a plausibility argument for why many low-level IF journals might have a virtually zero retraction index.<br />
<br />
4. For an exceedingly interesting interview with Fang and Casadevall see [http://www.nytimes.com/2012/04/17/science/rise-in-scientific-journal-retractions-prompts-calls-for-reform.html?pagewanted=all Carl Zimmer’s NYT article.] <br />
<blockquote><br />
Several factors are at play here, scientists say. One may be that because journals are now online, bad papers are simply reaching a wider audience, making it more likely that errors will be spotted. “You can sit at your laptop and pull a lot of different papers together,” Dr. Fang said.<br><br><br />
But other forces are more pernicious. To survive professionally, scientists feel the need to publish as many papers as possible, and to get them into high-profile journals. And sometimes they cut corners or even commit misconduct to get there.<br><br><br />
Each year, every laboratory produces a new crop of Ph.D.’s, who must compete for a small number of jobs, and the competition is getting fiercer. In 1973, more than half of biologists had a tenure-track job within six years of getting a Ph.D. By 2006 the figure was down to 15 percent. </blockquote><br />
The article is packed with intriguing discussion points about funding and ends with Fang’s pessimistic/realistic lament:<br />
<blockquote> “When our generation goes away, where is the new generation going to be?” he asked. “All the scientists I know are so anxious about their funding that they don’t make inspiring role models. I heard it from my own kids, who went into art and music respectively. They said, ‘You know, we see you, and you don’t look very happy.’ ” </blockquote><br />
<br />
Submitted by Paul Alper<br />
<br />
==Poker: Skill or luck?==<br />
[http://www.nytimes.com/2012/08/22/nyregion/poker-is-more-a-game-of-skill-than-of-chance-a-judge-rules.html?ref=opinion Poker is more a game of skill than of chance, a judge rules]<br><br />
by Mosi Secret, ''New York Times'', 21 August 2012<br />
<br />
[http://www.nytimes.com/2012/08/25/opinion/poker-an-american-pastime-and-a-game-of-skill.html?scp=1&sq=poker&st=Search No more bluffing]<br><br />
by James McManus, ''New York Times'', 24 August 2012<br />
<br />
This is not just a parlor debate. If poker is a game of skill rather than chance, then it cannot be regulated by laws governing gambling activity. See [http://test.causeweb.org/wiki/chance/index.php?title=Chance_News_46&action=edit&section=8 Chance News 46] for an earlier discussion of this topic.<br />
<br />
To be continued...<br />
<br />
Submitted by Bill Peterson<br />
<br />
==Where are the 47%?==<br />
[http://www.theatlanticcities.com/jobs-and-economy/2012/09/geography-47/3323/ The geography of the 47%]<br><br />
by Richard Florida, TheAtlanticCities.com, 19 September 2012<br />
<br />
The article included the scatterplot shown below. Each point represents a state. The full version from the Atlantic (available [http://charts.theatlantic.com/embed/505a126cbf96c4168d000035/ here]) is interactive: you can click on <br />
points to identify the state.<br />
:[[File:Nonpayers.png]]<br />
<br />
<br />
The [http://taxfoundation.org/article/nonpayers-state-2010 tax data] are available from the TaxFoundation.org. Note that non-payers<br />
are defined as those who filed tax returns indicating no liability. As explained in the article, there are other nonpayers who are not required to file (which is why there are no points on the plot at 47% or more!).<br />
<br />
Suggested by Margaret Cibes<br />
<br />
==The sexiest job==<br />
[http://hbr.org/2012/10/data-scientist-the-sexiest-job-of-the-21st-century/ Data scientist: The sexiest job of the 21st century]<br><br />
by Thomas H. Davenport and D.J. Patil , ''Harvard Business Review'', October 2012<br />
<br />
According to the article, the job title ''data scientist'' was "coined in 2008 by one of us, D.J. Patil, and Jeff Hammerbacher, then the respective leads of data and analytics efforts at LinkedIn and Facebook." <br />
<br />
Thanks to Nick Horton, who sent this link to the Isolated Statisticians list.<br />
<br />
==Skewed polling==<br />
[http://www.examiner.com/article/the-skewed-polls-issue-and-why-it-is-important-1 The skewed polls issue and why it is important]<br><br />
by Dean Chambers, Examiner.com, 25 September 2012<br />
<br />
Chambers has a website, [http://unskewedpolls.com UnskewedPolls], where he reanalyzes polls published by other organizations in order to adjust for <br />
what he sees as inherent bias. A number of recent polls have shown President Obama with a lead in key swing states. Chambers challenges these results on the basis that respondents who self-identify as Democrats comprise too large a proportion of the sample. By reweighting the results to reflect what he asserts are the true <br />
party proportions among all voters, Chambers finds that most polling data actually indicate that Romney is leading. Here is one example from the article<br />
<blockquote><br />
The Gallup tracking poll, which has been over-sampled Democrats in the past, has released its latest numbers today showing President Obama leading 48 percent to 45 percent for Mitt Romney. But the non-skewed uses a sample weighted by the expected partisan makeup of the electorate, the QStarNews Daily Tracking poll [Chambers's<br />
organization], shows Romney leading over Obama by a 53 percent to 45 percent margin. <br />
</blockquote><br />
Gallup's editor-in-chief, Frank Newport, responds to this issue in a recent post, [http://pollingmatters.gallup.com/2012/09/the-recurring-and-misleading-focus-on.html The recurring -- and misleading -- focus on party Identification] (27 September 2012). He says that Gallup determines party identification as part of its surveys, <br />
asking, “In politics, as of today, do you consider yourself a Republican, a Democrat, or an independent?" Thus, rather than reflecting fixed percentages, party affiliation is itself dynamic. In other words, what Chambers interprets as an over-sampling of Democrats may instead reflect increasing support for the Democratic candidate.<br />
<br />
==Tonight Show birthday problem==<br />
[http://opinionator.blogs.nytimes.com/2012/10/01/its-my-birthday-too-yeah/?hp It’s my birthday too, yeah]<br><br />
by Steven Strogatz, ''New York Times'', 1 October 2012<br />
<br />
We were very happy to learn that Steven Stogatz has returned to the ''Times'' with a new Opinionator series entitled <br />
[http://opinionator.blogs.nytimes.com/category/me-myself-and-math/ Me, Myself and Math] (his earlier series, [http://topics.nytimes.com/top/opinion/series/steven_strogatz_on_the_elements_of_math/index.html The Elements of Math], appeared in 2010).<br />
<br />
For the present piece, he has unearthed some wonderful [http://www.cornell.edu/video/?videoid=2334 archival video] of a Tonight Show episode from 1980, in which Johnny Carson<br />
and Ed McMahon attempt to validate the famous birthday problem probability using the studio audience. Alas, Ed inadvertently leads Johnny to confuse this with the "birthmate problem" (how<br />
many people do you need in a room to have a better than even chance of matching ''your'' birthday?). They wind up asking for the birthday of an audience member <br />
seated in the front row, and are then (comically) puzzled when no one else shares that birthday. But do watch the video--a verbal description doesn't do justice to Johnny's inimitable style.<br />
<br />
The surprising new revelation is that it was Carson himself who brought up the birthday problem! As described in the article, various retellings of the story over the years have inserted a guest mathematician/statistician whose attempt to explain the problem was derailed by the host. <br />
<br />
The notes at the end of the article provide some great pointers to further discussion and applications of the problem. <br />
<br />
Submitted by Bill Peterson<br />
<br />
==Correlation does not imply causation==<br />
Jeff Witmer posted this link to the Isolated Statisticians mailing list:<br />
<br />
[http://www.slate.com/articles/health_and_science/science/2012/10/correlation_does_not_imply_causation_how_the_internet_fell_in_love_with_a_stats_class_clich_.single.html The Internet blowhard’s favorite phrase]<br><br />
by Daniel Engber, ''Slate'', 2 October 2012<br />
<br />
This essay--subtitled "Why do people love to say that correlation does not imply causation?"--explores the origin of the phrase, and some pushback against its reflexive use in online debates.<br />
<br />
==Is someone cooking the unemployment numbers==<br />
<br />
[http://economix.blogs.nytimes.com/2012/10/05/explaining-the-big-gain-in-job-getters/ Taming volatile Raw Data for Jobs Reports], Catherine Rampbell, The New York Times, October 5, 2012.<br />
<br />
The most recent unemployment data is good, with a reported unemployment rate below 8%. That has some people upset. Many conservatives (e.g., [http://economix.blogs.nytimes.com/2012/10/05/from-jack-welch-a-conspiracy-theory/ Jack Welch], [http://www.huffingtonpost.com/2012/10/05/joe-scarborough-jobs-report-numbers_n_1942430.html Joe Scarborough], [http://newsbusters.org/blogs/mark-finkelstein/2012/10/05/santelli-smells-rat-i-told-you-theyd-get-it-under-8-they-did Rick Santelli]) expressed a concern that the Bureau of Labor Statistics (BLS) may have cheated in order to help get President Obama re-elected.<br />
<br />
These criticisms ignore the fact that all of the statisticians at BLS are not political appointees, but career civil servants who have a fair amount of insulation from political pressures. Catherine Rampbell points out some issues with unemployment figures that may lead to confusion.<br />
<br />
<blockquote>These numbers are always tremendously volatile, but the reasons are statistical, not political. The numbers come from a tiny survey with a margin of error of 400,000. Every month there are wild swings, and no one takes them at face value. The swings usually attract less attention, though, because the political stakes are usually lower.</blockquote><br />
<br />
Another issue is the use of seasonal adjustments. Unemployment rates do have predictable shifts based on the calendar. In particular, there is a large change in employment as younger workers leave their summer jobs and go back to college.<br />
<br />
<blockquote>The Bureau of Labor Statistics adjusts its raw survey data to correct for seasonal patterns, and since a decline in employment is expected for those 20 to 24, the economists at the bureau increased the level of employment for this group in the seasonally adjusted numbers.</blockquote><br />
<br />
It's possible that the seasonal adjustment was an overadjustment.<br />
<br />
<blockquote>Changes in seasonal patterns like this one can introduce more error into the headline numbers, and can at least partly explain why the overall change in household employment looked so much bigger in September than seems plausible.</blockquote><br />
<br />
Written by Steve Simon<br />
<br />
===Discussion===<br />
<br />
1. How can the BLS de-politicize its unemployment report?<br />
<br />
2. How can the media de-politicize the unemployment report?<br />
<br />
3. How well do you think the civil service system protects career civil servants from political pressures?</div>Simon66217https://www.causeweb.org/wiki/chance/index.php?title=Chance_News_88&diff=16489Chance News 882012-10-08T20:30:08Z<p>Simon66217: /* Is someone cooking the unemployment numbers */</p>
<hr />
<div>==Quotations==<br />
"If you are a passenger on a plane and the pilot tells you he has a faulty map, you get off the plane; you don’t stay and say 'well, there is nothing better.' But in economics, particularly finance, they keep teaching these models on grounds that 'there is nothing better,' causing harmful risk-taking."<br />
<br />
<div align=right>--Nassim Nicholas Taleb, in [http://www.nytimes.com/roomfordebate/2012/04/01/how-to-teach-economics-after-the-financial-crisis/throw-out-the-old-economic-models Throw out the probability models], ''New York Times'' Room for Debate, 2 April 2012</div><br />
<br />
==Forsooth==<br />
“Odds of becoming a top ranked NASCAR driver: 1 in 125 billion.”<br />
<div align=right>from an advertisement by Autism Speaks in ''Sports Illustrated''</div><br />
<br />
(There are only about 7 billion people in the world, so if there are only two “top ranked drivers” then the odds are only 1 in 3.5 billion or so.)<br />
<br />
Submitted by Marc Hurwitz<br />
<br />
==Impact and retract==<br />
<br />
As unlikely as it may seem, there are many thousands (!) of health/medical journals published each month. Obviously, some carry more clout than others when it comes to promotion and reputation of contributing authors. Those journals are said to have high “impact factors.” The de facto and default definition of IF, according to [http://en.wikipedia.org/wiki/Impact_factor Wikipedia] “was devised by Eugene Garfield, the founder of the Institute for Scientific Information (ISI), now part of Thomson Reuters. Impact factors are calculated yearly for those journals that are indexed in Thomson Reuters Journal Citation Reports.”<br />
<br />
The calculation of IF is a bit involved:<br />
<br />
<blockquote>In a given year, the impact factor of a journal is the average number of citations received per paper published in that journal during the two preceding years. For example, if a journal has an impact factor of 3 in 2008, then its papers published in 2006 and 2007 received 3 citations each on average in 2008. The 2008 impact factor of a journal would be calculated as follows:<br />
:''A'' = the number of times articles published in 2006 and 2007 were cited by indexed journals during 2008.<br />
:''B'' = the total number of "citable items" published by that journal in 2006 and 2007. ("Citable items" are usually articles, reviews, proceedings, or notes; not editorials or Letters-to-the-Editor.)<br />
:2008 impact factor = ''A/B''.<br />
(Note that 2008 impact factors are actually published in 2009; they cannot be calculated until all of the 2008 publications have been processed by the indexing agency.)</blockquote><br />
<br />
Of course, when there is an “''A'' over ''B''” you can be sure that some journals might be tempted to inflate ''A'' and/or lower ''B'' to obtain a higher IF.<br />
<br />
<blockquote>A journal can adopt editorial policies that increase its impact factor. For example, journals may publish a larger percentage of review articles which generally are cited more than research reports. Therefore review articles can raise the impact factor of the journal and review journals will therefore often have the highest impact factors in their respective fields. Journals may also attempt to limit the number of "citable items", ie the denominator of the IF equation, either by declining to publish articles (such as case reports in medical journals) which are unlikely to be cited or by altering articles (by not allowing an abstract or bibliography) in hopes that Thomson Scientific will not deem it a "citable item". (As a result of negotiations over whether items are "citable", impact factor variations of more than 300% have been observed.) </blockquote><br />
<br />
Then, there is “coercive citation” <br />
<br />
<blockquote>in which an editor forces an author to add spurious self-citations to an article before the journal will agree to publish it in order to inflate the journal's impact factor. </blockquote><br />
<br />
The pressure on a researcher to publish in high IF journals according to <br />
[http://blogs.lse.ac.uk/impactofsocialsciences/2011/12/19/impact-factor-citations-retractions/ Björn Brembs] is extremely high:<br />
<br />
<blockquote>As a scientist today, it is very difficult to find employment if you cannot sport publications in high-ranking journals. In the increasing competition for the coveted spots, it is starting to be difficult to find employment with only few papers in high-ranking journals: a consistent record of ‘high-impact’ publications is required if you want science to be able to put food on your table. Subjective impressions appear to support this intuitive notion: isn’t a lot of great research published in Science and Nature while we so often find horrible work published in little-known journals? Isn’t it a good thing that in times of shrinking budgets we only allow the very best scientists to continue spending taxpayer funds? </blockquote><br />
<br />
Ah, but Brembs then points out that as plausible as the above argument is regarding the superiority of high IF journals, the data do not support that statement. He refers to an article by [http://iai.asm.org/content/79/10/3855.full?maxtoshow=&hits=10&RESULTFORMAT=&fulltext=%25DF&searchid=1&FIRSTINDEX=2663&resourcetype=HWFIG Fang and Casadevall] from which he obtains this stunning regression graph:<br />
<br />
<center> [[File:Brembs.png]] </center><br />
<br />
The retraction index is the number of retractions in the journal from 2001 to 2010, multiplied by 1000, and divided by the number of published articles with abstracts. The p-value for slope is exceedingly small and the coefficient of determination is .77. Thus, “at least with the current data, IF indeed seems to be a more reliable predictor of retractions than of actual citations.” He reasons that<br />
<br />
<blockquote>If your livelihood depends on this Science/Nature paper, doesn’t the pressure increase to maybe forget this one crucial control experiment, or leave out some data points that don’t quite make the story look so nice? After all, you know your results are solid, it’s only cosmetics which are required to make it a top-notch publication! Of course, in science there never is certainty, so such behavior will decrease the reliability of the scientific reports being published. And indeed, together with the decrease in tenured positions, the number of retractions has increased at about 400-fold the rate of publication increase. </blockquote><br />
<br />
===Discussion===<br />
<br />
1. Obtain a (very) good dictionary to see how the grammatical uses of the word “impact” has differed down through the centuries with a shift taking place somewhere in the post-World-War-II world. Ask an elderly person for his view of “impact” as a verb let alone as an adjective. Do the same for the word “contact” which had a grammatical shift in the 1920s.<br />
<br />
2. The Fang and Casadevall paper had the graph presented this way:<br />
<br />
<center> [[File:FangCasadevallFig1.png]] </center><br />
<br />
Why is Brembs’ version more suggestive of a cause (IF) and effect (retraction index) relationship?<br />
<br />
3. Give a plausibility argument for why many low-level IF journals might have a virtually zero retraction index.<br />
<br />
4. For an exceedingly interesting interview with Fang and Casadevall see [http://www.nytimes.com/2012/04/17/science/rise-in-scientific-journal-retractions-prompts-calls-for-reform.html?pagewanted=all Carl Zimmer’s NYT article.] <br />
<blockquote><br />
Several factors are at play here, scientists say. One may be that because journals are now online, bad papers are simply reaching a wider audience, making it more likely that errors will be spotted. “You can sit at your laptop and pull a lot of different papers together,” Dr. Fang said.<br><br><br />
But other forces are more pernicious. To survive professionally, scientists feel the need to publish as many papers as possible, and to get them into high-profile journals. And sometimes they cut corners or even commit misconduct to get there.<br><br><br />
Each year, every laboratory produces a new crop of Ph.D.’s, who must compete for a small number of jobs, and the competition is getting fiercer. In 1973, more than half of biologists had a tenure-track job within six years of getting a Ph.D. By 2006 the figure was down to 15 percent. </blockquote><br />
The article is packed with intriguing discussion points about funding and ends with Fang’s pessimistic/realistic lament:<br />
<blockquote> “When our generation goes away, where is the new generation going to be?” he asked. “All the scientists I know are so anxious about their funding that they don’t make inspiring role models. I heard it from my own kids, who went into art and music respectively. They said, ‘You know, we see you, and you don’t look very happy.’ ” </blockquote><br />
<br />
Submitted by Paul Alper<br />
<br />
==Poker: Skill or luck?==<br />
[http://www.nytimes.com/2012/08/22/nyregion/poker-is-more-a-game-of-skill-than-of-chance-a-judge-rules.html?ref=opinion Poker is more a game of skill than of chance, a judge rules]<br><br />
by Mosi Secret, ''New York Times'', 21 August 2012<br />
<br />
[http://www.nytimes.com/2012/08/25/opinion/poker-an-american-pastime-and-a-game-of-skill.html?scp=1&sq=poker&st=Search No more bluffing]<br><br />
by James McManus, ''New York Times'', 24 August 2012<br />
<br />
This is not just a parlor debate. If poker is a game of skill rather than chance, then it cannot be regulated by laws governing gambling activity. See [http://test.causeweb.org/wiki/chance/index.php?title=Chance_News_46&action=edit&section=8 Chance News 46] for an earlier discussion of this topic.<br />
<br />
To be continued...<br />
<br />
Submitted by Bill Peterson<br />
<br />
==Where are the 47%?==<br />
[http://www.theatlanticcities.com/jobs-and-economy/2012/09/geography-47/3323/ The geography of the 47%]<br><br />
by Richard Florida, TheAtlanticCities.com, 19 September 2012<br />
<br />
The article included the scatterplot shown below. Each point represents a state. The full version from the Atlantic (available [http://charts.theatlantic.com/embed/505a126cbf96c4168d000035/ here]) is interactive: you can click on <br />
points to identify the state.<br />
:[[File:Nonpayers.png]]<br />
<br />
<br />
The [http://taxfoundation.org/article/nonpayers-state-2010 tax data] are available from the TaxFoundation.org. Note that non-payers<br />
are defined as those who filed tax returns indicating no liability. As explained in the article, there are other nonpayers who are not required to file (which is why there are no points on the plot at 47% or more!).<br />
<br />
Suggested by Margaret Cibes<br />
<br />
==The sexiest job==<br />
[http://hbr.org/2012/10/data-scientist-the-sexiest-job-of-the-21st-century/ Data scientist: The sexiest job of the 21st century]<br><br />
by Thomas H. Davenport and D.J. Patil , ''Harvard Business Review'', October 2012<br />
<br />
According to the article, the job title ''data scientist'' was "coined in 2008 by one of us, D.J. Patil, and Jeff Hammerbacher, then the respective leads of data and analytics efforts at LinkedIn and Facebook." <br />
<br />
Thanks to Nick Horton, who sent this link to the Isolated Statisticians list.<br />
<br />
==Skewed polling==<br />
[http://www.examiner.com/article/the-skewed-polls-issue-and-why-it-is-important-1 The skewed polls issue and why it is important]<br><br />
by Dean Chambers, Examiner.com, 25 September 2012<br />
<br />
Chambers has a website, [http://unskewedpolls.com UnskewedPolls], where he reanalyzes polls published by other organizations in order to adjust for <br />
what he sees as inherent bias. A number of recent polls have shown President Obama with a lead in key swing states. Chambers challenges these results on the basis that respondents who self-identify as Democrats comprise too large a proportion of the sample. By reweighting the results to reflect what he asserts are the true <br />
party proportions among all voters, Chambers finds that most polling data actually indicate that Romney is leading. Here is one example from the article<br />
<blockquote><br />
The Gallup tracking poll, which has been over-sampled Democrats in the past, has released its latest numbers today showing President Obama leading 48 percent to 45 percent for Mitt Romney. But the non-skewed uses a sample weighted by the expected partisan makeup of the electorate, the QStarNews Daily Tracking poll [Chambers's<br />
organization], shows Romney leading over Obama by a 53 percent to 45 percent margin. <br />
</blockquote><br />
Gallup's editor-in-chief, Frank Newport, responds to this issue in a recent post, [http://pollingmatters.gallup.com/2012/09/the-recurring-and-misleading-focus-on.html The recurring -- and misleading -- focus on party Identification] (27 September 2012). He says that Gallup determines party identification as part of its surveys, <br />
asking, “In politics, as of today, do you consider yourself a Republican, a Democrat, or an independent?" Thus, rather than reflecting fixed percentages, party affiliation is itself dynamic. In other words, what Chambers interprets as an over-sampling of Democrats may instead reflect increasing support for the Democratic candidate.<br />
<br />
==Tonight Show birthday problem==<br />
[http://opinionator.blogs.nytimes.com/2012/10/01/its-my-birthday-too-yeah/?hp It’s my birthday too, yeah]<br><br />
by Steven Strogatz, ''New York Times'', 1 October 2012<br />
<br />
We were very happy to learn that Steven Stogatz has returned to the ''Times'' with a new Opinionator series entitled <br />
[http://opinionator.blogs.nytimes.com/category/me-myself-and-math/ Me, Myself and Math] (his earlier series, [http://topics.nytimes.com/top/opinion/series/steven_strogatz_on_the_elements_of_math/index.html The Elements of Math], appeared in 2010).<br />
<br />
For the present piece, he has unearthed some wonderful [http://www.cornell.edu/video/?videoid=2334 archival video] of a Tonight Show episode from 1980, in which Johnny Carson<br />
and Ed McMahon attempt to validate the famous birthday problem probability using the studio audience. Alas, Ed inadvertently leads Johnny to confuse this with the "birthmate problem" (how<br />
many people do you need in a room to have a better than even chance of matching ''your'' birthday?). They wind up asking for the birthday of an audience member <br />
seated in the front row, and are then (comically) puzzled when no one else shares that birthday. But do watch the video--a verbal description doesn't do justice to Johnny's inimitable style.<br />
<br />
The surprising new revelation is that it was Carson himself who brought up the birthday problem! As described in the article, various retellings of the story over the years have inserted a guest mathematician/statistician whose attempt to explain the problem was derailed by the host. <br />
<br />
The notes at the end of the article provide some great pointers to further discussion and applications of the problem. <br />
<br />
Submitted by Bill Peterson<br />
<br />
==Correlation does not imply causation==<br />
Jeff Witmer posted this link to the Isolated Statisticians mailing list:<br />
<br />
[http://www.slate.com/articles/health_and_science/science/2012/10/correlation_does_not_imply_causation_how_the_internet_fell_in_love_with_a_stats_class_clich_.single.html The Internet blowhard’s favorite phrase]<br><br />
by Daniel Engber, ''Slate'', 2 October 2012<br />
<br />
This essay--subtitled "Why do people love to say that correlation does not imply causation?"--explores the origin of the phrase, and some pushback against its reflexive use in online debates.<br />
<br />
==Is someone cooking the unemployment numbers==<br />
<br />
[http://economix.blogs.nytimes.com/2012/10/05/explaining-the-big-gain-in-job-getters/ Taming volatile Raw Data for Jobs Reports], Catherine Rampbell, The New York Times, October 5, 2012.<br />
<br />
The most recent unemployment data is good, with a reported unemployment rate below 8%. That has some people upset. Many conservatives (e.g., [http://economix.blogs.nytimes.com/2012/10/05/from-jack-welch-a-conspiracy-theory/ Jack Welch], [http://www.huffingtonpost.com/2012/10/05/joe-scarborough-jobs-report-numbers_n_1942430.html Joe Scarborough], [http://newsbusters.org/blogs/mark-finkelstein/2012/10/05/santelli-smells-rat-i-told-you-theyd-get-it-under-8-they-did Rick Santelli]) expressed a concern that the Bureau of Labor Statistics (BLS) may have cheated in order to help get President Obama re-elected.<br />
<br />
These criticisms ignore the fact that all of the statisticians at BLS are not political appointees, but career civil servants who have a fair amount of insulation from political pressures. Catherine Rampbell points out some issues with unemployment figures that may lead to confusion.<br />
<br />
<blockquote>These numbers are always tremendously volatile, but the reasons are statistical, not political. The numbers come from a tiny survey with a margin of error of 400,000. Every month there are wild swings, and no one takes them at face value. The swings usually attract less attention, though, because the political stakes are usually lower.</blockquote><br />
<br />
Another issue is the use of seasonal adjustments. Unemployment rates do have predictable shifts based on the calendar. In particular, there is a large change in employment as younger workers leave their summer jobs and go back to college.<br />
<br />
<blockquote>The Bureau of Labor Statistics adjusts its raw survey data to correct for seasonal patterns, and since a decline in employment is expected for those 20 to 24, the economists at the bureau increased the level of employment for this group in the seasonally adjusted numbers.</blockquote><br />
<br />
It's possible that the seasonal adjustment was an overadjustment.<br />
<br />
<blockquote>Changes in seasonal patterns like this one can introduce more error into the headline numbers, and can at least partly explain why the overall change in household employment looked so much bigger in September than seems plausible.</blockquote><br />
<br />
Written by Steve Simon<br />
<br />
===Questions===<br />
<br />
1. How can the BLS de-politicize its unemployment report?<br />
<br />
2. How can the media de-politicize the unemployment report?<br />
<br />
3. How well do you think the civil service system protects career civil servants from political pressures?</div>Simon66217https://www.causeweb.org/wiki/chance/index.php?title=Chance_News_88&diff=16488Chance News 882012-10-08T20:26:34Z<p>Simon66217: /* Correlation does not imply causation */</p>
<hr />
<div>==Quotations==<br />
"If you are a passenger on a plane and the pilot tells you he has a faulty map, you get off the plane; you don’t stay and say 'well, there is nothing better.' But in economics, particularly finance, they keep teaching these models on grounds that 'there is nothing better,' causing harmful risk-taking."<br />
<br />
<div align=right>--Nassim Nicholas Taleb, in [http://www.nytimes.com/roomfordebate/2012/04/01/how-to-teach-economics-after-the-financial-crisis/throw-out-the-old-economic-models Throw out the probability models], ''New York Times'' Room for Debate, 2 April 2012</div><br />
<br />
==Forsooth==<br />
“Odds of becoming a top ranked NASCAR driver: 1 in 125 billion.”<br />
<div align=right>from an advertisement by Autism Speaks in ''Sports Illustrated''</div><br />
<br />
(There are only about 7 billion people in the world, so if there are only two “top ranked drivers” then the odds are only 1 in 3.5 billion or so.)<br />
<br />
Submitted by Marc Hurwitz<br />
<br />
==Impact and retract==<br />
<br />
As unlikely as it may seem, there are many thousands (!) of health/medical journals published each month. Obviously, some carry more clout than others when it comes to promotion and reputation of contributing authors. Those journals are said to have high “impact factors.” The de facto and default definition of IF, according to [http://en.wikipedia.org/wiki/Impact_factor Wikipedia] “was devised by Eugene Garfield, the founder of the Institute for Scientific Information (ISI), now part of Thomson Reuters. Impact factors are calculated yearly for those journals that are indexed in Thomson Reuters Journal Citation Reports.”<br />
<br />
The calculation of IF is a bit involved:<br />
<br />
<blockquote>In a given year, the impact factor of a journal is the average number of citations received per paper published in that journal during the two preceding years. For example, if a journal has an impact factor of 3 in 2008, then its papers published in 2006 and 2007 received 3 citations each on average in 2008. The 2008 impact factor of a journal would be calculated as follows:<br />
:''A'' = the number of times articles published in 2006 and 2007 were cited by indexed journals during 2008.<br />
:''B'' = the total number of "citable items" published by that journal in 2006 and 2007. ("Citable items" are usually articles, reviews, proceedings, or notes; not editorials or Letters-to-the-Editor.)<br />
:2008 impact factor = ''A/B''.<br />
(Note that 2008 impact factors are actually published in 2009; they cannot be calculated until all of the 2008 publications have been processed by the indexing agency.)</blockquote><br />
<br />
Of course, when there is an “''A'' over ''B''” you can be sure that some journals might be tempted to inflate ''A'' and/or lower ''B'' to obtain a higher IF.<br />
<br />
<blockquote>A journal can adopt editorial policies that increase its impact factor. For example, journals may publish a larger percentage of review articles which generally are cited more than research reports. Therefore review articles can raise the impact factor of the journal and review journals will therefore often have the highest impact factors in their respective fields. Journals may also attempt to limit the number of "citable items", ie the denominator of the IF equation, either by declining to publish articles (such as case reports in medical journals) which are unlikely to be cited or by altering articles (by not allowing an abstract or bibliography) in hopes that Thomson Scientific will not deem it a "citable item". (As a result of negotiations over whether items are "citable", impact factor variations of more than 300% have been observed.) </blockquote><br />
<br />
Then, there is “coercive citation” <br />
<br />
<blockquote>in which an editor forces an author to add spurious self-citations to an article before the journal will agree to publish it in order to inflate the journal's impact factor. </blockquote><br />
<br />
The pressure on a researcher to publish in high IF journals according to <br />
[http://blogs.lse.ac.uk/impactofsocialsciences/2011/12/19/impact-factor-citations-retractions/ Björn Brembs] is extremely high:<br />
<br />
<blockquote>As a scientist today, it is very difficult to find employment if you cannot sport publications in high-ranking journals. In the increasing competition for the coveted spots, it is starting to be difficult to find employment with only few papers in high-ranking journals: a consistent record of ‘high-impact’ publications is required if you want science to be able to put food on your table. Subjective impressions appear to support this intuitive notion: isn’t a lot of great research published in Science and Nature while we so often find horrible work published in little-known journals? Isn’t it a good thing that in times of shrinking budgets we only allow the very best scientists to continue spending taxpayer funds? </blockquote><br />
<br />
Ah, but Brembs then points out that as plausible as the above argument is regarding the superiority of high IF journals, the data do not support that statement. He refers to an article by [http://iai.asm.org/content/79/10/3855.full?maxtoshow=&hits=10&RESULTFORMAT=&fulltext=%25DF&searchid=1&FIRSTINDEX=2663&resourcetype=HWFIG Fang and Casadevall] from which he obtains this stunning regression graph:<br />
<br />
<center> [[File:Brembs.png]] </center><br />
<br />
The retraction index is the number of retractions in the journal from 2001 to 2010, multiplied by 1000, and divided by the number of published articles with abstracts. The p-value for slope is exceedingly small and the coefficient of determination is .77. Thus, “at least with the current data, IF indeed seems to be a more reliable predictor of retractions than of actual citations.” He reasons that<br />
<br />
<blockquote>If your livelihood depends on this Science/Nature paper, doesn’t the pressure increase to maybe forget this one crucial control experiment, or leave out some data points that don’t quite make the story look so nice? After all, you know your results are solid, it’s only cosmetics which are required to make it a top-notch publication! Of course, in science there never is certainty, so such behavior will decrease the reliability of the scientific reports being published. And indeed, together with the decrease in tenured positions, the number of retractions has increased at about 400-fold the rate of publication increase. </blockquote><br />
<br />
===Discussion===<br />
<br />
1. Obtain a (very) good dictionary to see how the grammatical uses of the word “impact” has differed down through the centuries with a shift taking place somewhere in the post-World-War-II world. Ask an elderly person for his view of “impact” as a verb let alone as an adjective. Do the same for the word “contact” which had a grammatical shift in the 1920s.<br />
<br />
2. The Fang and Casadevall paper had the graph presented this way:<br />
<br />
<center> [[File:FangCasadevallFig1.png]] </center><br />
<br />
Why is Brembs’ version more suggestive of a cause (IF) and effect (retraction index) relationship?<br />
<br />
3. Give a plausibility argument for why many low-level IF journals might have a virtually zero retraction index.<br />
<br />
4. For an exceedingly interesting interview with Fang and Casadevall see [http://www.nytimes.com/2012/04/17/science/rise-in-scientific-journal-retractions-prompts-calls-for-reform.html?pagewanted=all Carl Zimmer’s NYT article.] <br />
<blockquote><br />
Several factors are at play here, scientists say. One may be that because journals are now online, bad papers are simply reaching a wider audience, making it more likely that errors will be spotted. “You can sit at your laptop and pull a lot of different papers together,” Dr. Fang said.<br><br><br />
But other forces are more pernicious. To survive professionally, scientists feel the need to publish as many papers as possible, and to get them into high-profile journals. And sometimes they cut corners or even commit misconduct to get there.<br><br><br />
Each year, every laboratory produces a new crop of Ph.D.’s, who must compete for a small number of jobs, and the competition is getting fiercer. In 1973, more than half of biologists had a tenure-track job within six years of getting a Ph.D. By 2006 the figure was down to 15 percent. </blockquote><br />
The article is packed with intriguing discussion points about funding and ends with Fang’s pessimistic/realistic lament:<br />
<blockquote> “When our generation goes away, where is the new generation going to be?” he asked. “All the scientists I know are so anxious about their funding that they don’t make inspiring role models. I heard it from my own kids, who went into art and music respectively. They said, ‘You know, we see you, and you don’t look very happy.’ ” </blockquote><br />
<br />
Submitted by Paul Alper<br />
<br />
==Poker: Skill or luck?==<br />
[http://www.nytimes.com/2012/08/22/nyregion/poker-is-more-a-game-of-skill-than-of-chance-a-judge-rules.html?ref=opinion Poker is more a game of skill than of chance, a judge rules]<br><br />
by Mosi Secret, ''New York Times'', 21 August 2012<br />
<br />
[http://www.nytimes.com/2012/08/25/opinion/poker-an-american-pastime-and-a-game-of-skill.html?scp=1&sq=poker&st=Search No more bluffing]<br><br />
by James McManus, ''New York Times'', 24 August 2012<br />
<br />
This is not just a parlor debate. If poker is a game of skill rather than chance, then it cannot be regulated by laws governing gambling activity. See [http://test.causeweb.org/wiki/chance/index.php?title=Chance_News_46&action=edit&section=8 Chance News 46] for an earlier discussion of this topic.<br />
<br />
To be continued...<br />
<br />
Submitted by Bill Peterson<br />
<br />
==Where are the 47%?==<br />
[http://www.theatlanticcities.com/jobs-and-economy/2012/09/geography-47/3323/ The geography of the 47%]<br><br />
by Richard Florida, TheAtlanticCities.com, 19 September 2012<br />
<br />
The article included the scatterplot shown below. Each point represents a state. The full version from the Atlantic (available [http://charts.theatlantic.com/embed/505a126cbf96c4168d000035/ here]) is interactive: you can click on <br />
points to identify the state.<br />
:[[File:Nonpayers.png]]<br />
<br />
<br />
The [http://taxfoundation.org/article/nonpayers-state-2010 tax data] are available from the TaxFoundation.org. Note that non-payers<br />
are defined as those who filed tax returns indicating no liability. As explained in the article, there are other nonpayers who are not required to file (which is why there are no points on the plot at 47% or more!).<br />
<br />
Suggested by Margaret Cibes<br />
<br />
==The sexiest job==<br />
[http://hbr.org/2012/10/data-scientist-the-sexiest-job-of-the-21st-century/ Data scientist: The sexiest job of the 21st century]<br><br />
by Thomas H. Davenport and D.J. Patil , ''Harvard Business Review'', October 2012<br />
<br />
According to the article, the job title ''data scientist'' was "coined in 2008 by one of us, D.J. Patil, and Jeff Hammerbacher, then the respective leads of data and analytics efforts at LinkedIn and Facebook." <br />
<br />
Thanks to Nick Horton, who sent this link to the Isolated Statisticians list.<br />
<br />
==Skewed polling==<br />
[http://www.examiner.com/article/the-skewed-polls-issue-and-why-it-is-important-1 The skewed polls issue and why it is important]<br><br />
by Dean Chambers, Examiner.com, 25 September 2012<br />
<br />
Chambers has a website, [http://unskewedpolls.com UnskewedPolls], where he reanalyzes polls published by other organizations in order to adjust for <br />
what he sees as inherent bias. A number of recent polls have shown President Obama with a lead in key swing states. Chambers challenges these results on the basis that respondents who self-identify as Democrats comprise too large a proportion of the sample. By reweighting the results to reflect what he asserts are the true <br />
party proportions among all voters, Chambers finds that most polling data actually indicate that Romney is leading. Here is one example from the article<br />
<blockquote><br />
The Gallup tracking poll, which has been over-sampled Democrats in the past, has released its latest numbers today showing President Obama leading 48 percent to 45 percent for Mitt Romney. But the non-skewed uses a sample weighted by the expected partisan makeup of the electorate, the QStarNews Daily Tracking poll [Chambers's<br />
organization], shows Romney leading over Obama by a 53 percent to 45 percent margin. <br />
</blockquote><br />
Gallup's editor-in-chief, Frank Newport, responds to this issue in a recent post, [http://pollingmatters.gallup.com/2012/09/the-recurring-and-misleading-focus-on.html The recurring -- and misleading -- focus on party Identification] (27 September 2012). He says that Gallup determines party identification as part of its surveys, <br />
asking, “In politics, as of today, do you consider yourself a Republican, a Democrat, or an independent?" Thus, rather than reflecting fixed percentages, party affiliation is itself dynamic. In other words, what Chambers interprets as an over-sampling of Democrats may instead reflect increasing support for the Democratic candidate.<br />
<br />
==Tonight Show birthday problem==<br />
[http://opinionator.blogs.nytimes.com/2012/10/01/its-my-birthday-too-yeah/?hp It’s my birthday too, yeah]<br><br />
by Steven Strogatz, ''New York Times'', 1 October 2012<br />
<br />
We were very happy to learn that Steven Stogatz has returned to the ''Times'' with a new Opinionator series entitled <br />
[http://opinionator.blogs.nytimes.com/category/me-myself-and-math/ Me, Myself and Math] (his earlier series, [http://topics.nytimes.com/top/opinion/series/steven_strogatz_on_the_elements_of_math/index.html The Elements of Math], appeared in 2010).<br />
<br />
For the present piece, he has unearthed some wonderful [http://www.cornell.edu/video/?videoid=2334 archival video] of a Tonight Show episode from 1980, in which Johnny Carson<br />
and Ed McMahon attempt to validate the famous birthday problem probability using the studio audience. Alas, Ed inadvertently leads Johnny to confuse this with the "birthmate problem" (how<br />
many people do you need in a room to have a better than even chance of matching ''your'' birthday?). They wind up asking for the birthday of an audience member <br />
seated in the front row, and are then (comically) puzzled when no one else shares that birthday. But do watch the video--a verbal description doesn't do justice to Johnny's inimitable style.<br />
<br />
The surprising new revelation is that it was Carson himself who brought up the birthday problem! As described in the article, various retellings of the story over the years have inserted a guest mathematician/statistician whose attempt to explain the problem was derailed by the host. <br />
<br />
The notes at the end of the article provide some great pointers to further discussion and applications of the problem. <br />
<br />
Submitted by Bill Peterson<br />
<br />
==Correlation does not imply causation==<br />
Jeff Witmer posted this link to the Isolated Statisticians mailing list:<br />
<br />
[http://www.slate.com/articles/health_and_science/science/2012/10/correlation_does_not_imply_causation_how_the_internet_fell_in_love_with_a_stats_class_clich_.single.html The Internet blowhard’s favorite phrase]<br><br />
by Daniel Engber, ''Slate'', 2 October 2012<br />
<br />
This essay--subtitled "Why do people love to say that correlation does not imply causation?"--explores the origin of the phrase, and some pushback against its reflexive use in online debates.<br />
<br />
==Is someone cooking the unemployment numbers==<br />
<br />
[http://economix.blogs.nytimes.com/2012/10/05/explaining-the-big-gain-in-job-getters/ Taming volatile Raw Data for Jobs Reports], Catherine Rampbell, The New York Times, October 5, 2012.<br />
<br />
The most recent unemployment data is good, with a reported unemployment rate below 8%. That has some people upset. Many conservatives (e.g., [http://economix.blogs.nytimes.com/2012/10/05/from-jack-welch-a-conspiracy-theory/ Jack Welch], [http://www.huffingtonpost.com/2012/10/05/joe-scarborough-jobs-report-numbers_n_1942430.html Joe Scarborough], [http://newsbusters.org/blogs/mark-finkelstein/2012/10/05/santelli-smells-rat-i-told-you-theyd-get-it-under-8-they-did Rick Santelli]) expressed a concern that the Bureau of Labor Statistics (BLS) may have cheated in order to help get President Obama re-elected.<br />
<br />
These criticisms ignore the fact that all of the statisticians at BLS are not political appointees, but career civil servants who have a fair amount of insulation from political pressures. Catherine Rampbell points out some issues with unemployment figures that may lead to confusion.<br />
<br />
<blockquote>These numbers are always tremendously volatile, but the reasons are statistical, not political. The numbers come from a tiny survey with a margin of error of 400,000. Every month there are wild swings, and no one takes them at face value. The swings usually attract less attention, though, because the political stakes are usually lower.</blockquote><br />
<br />
Another issue is the use of seasonal adjustments. Unemployment rates do have predictable shifts based on the calendar. In particular, there is a large change in employment as younger workers leave their summer jobs and go back to college.<br />
<br />
<blockquote>The Bureau of Labor Statistics adjusts its raw survey data to correct for seasonal patterns, and since a decline in employment is expected for those 20 to 24, the economists at the bureau increased the level of employment for this group in the seasonally adjusted numbers.</blockquote><br />
<br />
It's possible that the seasonal adjustment was an overadjustment.<br />
<br />
<blockquote>Changes in seasonal patterns like this one can introduce more error into the headline numbers, and can at least partly explain why the overall change in household employment looked so much bigger in September than seems plausible.</blockquote><br />
<br />
Written by Steve Simon</div>Simon66217https://www.causeweb.org/wiki/chance/index.php?title=Chance_News_86&diff=16136Chance News 862012-07-09T18:44:24Z<p>Simon66217: /* Crowdsourcing and its failed prediction on health care law */</p>
<hr />
<div>==Quotations==<br />
"Asymptotically we are all dead."<br />
<div align=right>--paraphrase of Keynes, sometimes attributed to Melvin R. Novick</div><br />
<br />
Submitted by Paul Alper<br />
<br />
----<br />
"To err is human, to forgive divine but to include errors in your design is statistical."<br />
<div align=right>--Leslie Kish, in [http://asapresidentialpapers.info/documents/Kish_Leslie_1977_edit_(wla_092809).pdf Chance, statistics, and statisticians], 1977 ASA Presidential Address</div><br />
<br />
Submitted by Bill Peterson<br />
<br />
----<br />
"The only statistical test one ever needs is the IOTT or 'interocular trauma test.' The result just hits one between the eyes. If one needs any more statistical analysis, one should be working harder to control sources of error, or perhaps studying something else entirely."<br />
<br />
<div align=right>--David H. Krantz, in [http://www.unt.edu/rss/class/mike/5030/articles/krantznhst.pdf The null hypothesis testing in psychology], ''JASA'', December 1999, p. 1373.</div><br />
<br />
Krantz is describing how some psychologists view statistical testing. On the same page he describes another viewpoint:<br />
<br />
:"Nothing is due to chance. This is the Freudian stance...but fortunately, it has little support among researchers."<br />
<br />
Submitted by Paul Alper<br />
<br />
==Forsooth==<br />
“The survey interviewed 991 Americans online from June 28-30. The precision of the Reuters/Ipsos online polls is measured using a <i>credibility</i> interval. In this case, the poll has a <i>credibility</i> interval of plus or minus 3.6 percentage points.” (emphasis added)<br />
<div align=right>[http://www.huffingtonpost.com/2012/07/01/obamacare-supreme-court-ruling_n_1641560.html?ref=topbar “Obamacare Support Rises After Supreme Court Ruling, Poll Finds”]<br><br />
<i>Huffington Post</i>, July 1, 2012</div><br />
<br />
Submitted by Margaret Cibes<br />
<br />
==Gaydar==<br />
[http://www.nytimes.com/2012/06/03/opinion/sunday/the-science-of-gaydar.html The science of ‘gaydar’]<br><br />
by Joshua A. Tabak and Vivian Zayas, ''New York Times'', 3 June 2012<br />
<br />
The definition of GAYDAR is the "Ability to sense a homosexual" according to [http://www.internetslang.com/GAYDAR-meaning-definition.asp internetslang.com.] <br />
<br />
In their NYT article, Tabak and Zayas write<br />
<blockquote><br />
Should you trust your gaydar in everyday life? Probably not. In our experiments, average gaydar judgment accuracy was only in the 60 percent range. This demonstrates gaydar ability — which is far from judgment proficiency. But is gaydar real? Absolutely. <br />
</blockquote><br />
At [http://www.plosone.org/article/info%3Adoi%2F10.1371%2Fjournal.pone.0036671 PLoS One] is their complete research paper where subjects viewed facial photographs of homosexuals and straights; the subjects then had a short time to decide the sexual orientation. In their first experiment, the faces were shown upright only:<br />
<blockquote><br />
Twenty-four University of Washington students (19 women; age range = 18–22 years) participated in exchange for extra course credit. Data from seven additional participants were excluded from analyses due to failure to follow instructions (n = 4) or computer malfunction (n = 3). <br />
</blockquote><br />
<br />
In the second experiment, faces were shown upright and upside-down and the subjects had a short time to decide the sexual orientation:<br />
<blockquote><br />
One hundred twenty-nine University of Washington students (92 women; age range = 18–25 years) participated in exchange for extra course credit. Data from 16 additional participants were excluded from analyses due to failure to follow instructions (n = 12) or average reaction times more than 3 SD above the mean (n = 4). </blockquote><br />
<br />
According to the authors, “there are two components of “accuracy”: the hit rate which is “the proportion of gay faces correctly perceived as gay, and the false alarm rate” which is “the proportion of straight faces incorrectly perceived as gay.” The figure reproduced below (full version [http://www.plosone.org/article/slideshow.action?uri=info:doi/10.1371/journal.pone.0036671&imageURI=info:doi/10.1371/journal.pone.0036671.g003 here]) indicates that the accuracy was better when the target gender was women as opposed to men and the accuracy was better when the faces were upright as opposed to upside down. Presumably, random guessing would produce an “accuracy” of about .5.<br />
<br />
::[[File:TabakZayas_fig3.png ]]<br />
<br />
::Figure 3. Accuracy of detecting sexual orientation from upright and upside-down faces (Experiment 2).<br />
::Mean accuracy (A′) in judging sexual orientation from faces presented for 50 milliseconds as a function of the target’s gender and spatial orientation (upright or upside-down; Experiment 2). Judgments of upright faces are based on both configural and featural processing, whereas judgments of upside-down faces are based only on featural face processing. Error bars represent ±1 SEM.<br />
<br />
Reproduced below are the detailed results for the second experiment:<br />
::[[File:TabakZayas_table1.png]]<br />
<br />
::Table 1. Hit and False Alarm Rates for Snap Judgments of Sexual Orientation in Experiment 2.<br />
<br />
The author’s conclude with the following statement:<br />
<blockquote><br />
<br />
The present research is the first to demonstrate (a) that configural face processing significantly contributes to perception of sexual orientation, and (b) that sexual orientation is inferred more easily from women’s vs. men’s faces. In light of these findings, it is interesting to note the popular desire to learn to read faces like books. Considering how challenging it is to read a book upside-down, it seems that we read faces better than we read books.<br />
</blockquote><br />
<br />
===Discussion===<br />
<br />
1. Gaydar "accuracy" seems to be defined in the paper as hit rate / (hit rate + false alarm rate) or, to use terms common in [http://en.wikipedia.org/wiki/Sensitivity_and_specificity medical tests,] positive predictive value = true positive / (true positive + false positive). The paper makes no mention of negative predictive value = true negative / (true negative + false negative). As is illustrated in the Wikipedia article, legitimate medical tests will tolerate a low positive predictive value because a more expensive test exists in the rare case that the disease is actually present; negative predictive values must be high to avoid potentially deadly false optimism. The situation here is somewhat different because the subjects were exposed to an approximately equal number of gays and straights whereas in medical tests, most people in the population do not have the “disease.”<br />
<br />
2. Perhaps the analogy with medical testing is inappropriate. That is, an error is an error and no distinction should be made between the two types of errors. Consider the above table for the case of women and upright spatial orientation. The hit rate is .36 and the false alarm rate is .22. If we assume that the 67 subjects viewed 100 gay faces and 100 straight faces, then we obtain the following table for average values:<br />
<br />
<center><br />
{| class="wikitable" style="text-align:center; width:50%;"<br />
|-<br />
! scope="col" | Orientation<br />
! scope="col" | +<br />
! scope="col" | -<br />
! scope="col" | Total<br />
|-<br />
! scope="row" | Gay<br />
| 36<br />
| 64<br />
| 100<br />
|-<br />
! scope="row" | Straight<br />
| 22<br />
| 78<br />
| 100<br />
|-<br />
! scope="row" | Total<br />
| 58<br />
| 142<br />
| 200<br />
|}<br />
</center><br />
This leads to Prob (success) = (36 + 78)/200 = .57; Prob (error) = (64 +22)/200 = .43<br />
In effect, the model could be hidden tosses of a coin and the subjects, in an ESP fashion, guess heads or tails before the toss. Of course, a Bayesian would then assume a prior distribution and combine that with the results of the study to obtain a posterior probability and avoid any mention of p-value based on .5 as the null.<br />
<br />
3. Why might the following be a useful way of assessing gaydar via a modification of the procedure used in the article? Repeat the second experiment with no gays.<br />
<br />
4. Why might the following be a useful way of assessing gaydar via a modification of the procedure used in the article? Repeat the second experiment with no straights.<br />
<br />
5. In case the reader feels that Discussion #3 and #4 are deceptive, see<br />
[http://www.psychwiki.com/wiki/Deception_(methodological_technique) this Psychwiki] which looks at the history of deception in social psychology:<br />
<blockquote><br />
deception can often be seen in the “cover story” for the study, which provides the participant with a justification for the procedures and measures used. The ultimate goal of using deception in research is to ensure that the behaviors or reactions observed in a controlled laboratory setting are as close to those behaviors and reactions that occur outside of the laboratory setting. </blockquote><br />
<br />
Submitted by Paul Alper<br />
<br />
==Coin experiments==<br />
[http://online.wsj.com/article/SB10001424052702303753904577454431281272936.html?KEYWORDS=boese “The Pleasures of Suffering for Science”], <i>The Wall Street Journal</i>, June 8, 2012<br><br />
<br />
This story, about scientists experimenting on themselves, included a reference to a coin-tossing experiment:<br />
<blockquote>Even mathematics offers an example of physical self-sacrifice, through repetitive stress injury. University of Georgia professor Pope R. Hill flipped a coin 100,000 times to prove that heads and tails would come up an approximately equal number of times. The experiment lasted a year. He fell sick but completed the count, though he had to enlist the aid of an assistant near the end.</blockquote><br />
A Google search for Prof. Hill turned up the following story at the [http://www.weirduniverse.net/blog/comments/testing_the_law_of_probability “Weird Science”] website:<br />
<blockquote> If you repeatedly flip a coin, the law of probability states that approximately half the time you should get heads and half the time tails. But does this law hold true in practice?<br> <br />
Pope R. Hill, a professor at the University of Georgia during the 1930s, wanted to find out. But he thought coin-flipping was too imprecise a measurement, since any one coin might be imbalanced, causing it to favor heads or tails.<br><br />
Instead, he filled a can with 200 pennies. Half were dated 1919, half dated 1920. He shook up the can, withdrew a coin, and recorded its date. Then he returned the coin to the can. He repeated this procedure 100,000 times!<br><br />
Of the 100,000 draws, 50,145 came out 1920. 49,855 came out 1919. Hill concluded that the law of half and half does work out in practice.</blockquote><br />
<br />
===Discussion===<br />
1. Do you think that drawing a single coin from among 1919 and 1920 coins - even in a perfectly shaken can - would solve the problem of potential imbalance between heads and tails on any single coin toss? Can you think of any possible imbalance in the former case?<br><br />
2. In the second story, there is a remarkable relationship between Hill’s final counts. What questions, if any, might it raise in your mind about the experiment?<br><br />
3. Which is the more accurate expectation from a coin-tossing experiment: (a) “heads and tails would come up an approximately equal number of times” (first story) or (b) “approximately half the time you should get heads and half the time tails” (second story)?<br><br />
<br />
Submitted by Margaret Cibes<br />
<br />
'''Note''': In the [http://www.dartmouth.edu/~chance/chance_news/recent_news/chance_news_11.01.html#item8 archives of the Chance Newsletter] there is a description of some other historical attempts to empirically demonstrate the chance of heads. We read there:<br />
<blockquote>The French naturalist Count Buffon (1707-1788), know to us from the Buffon needle problem, tossed a coin 4040 times with heads coming up 2048 or 50.693 percent of the time. Karl Pearson tossed a coin 24,000 times with head coming up 12,012 or 50.05 percent of the time. While imprisoned by the Germans in the second world war, South African mathematician John Kerrich tossed a coin 10,000 times with heads coming up 5067 or 50.67 percent of the time. You can find his data in the classic text, ''Statistics'', by Freedman, Pisani and Purves.</blockquote><br />
<br />
==New presidential poll may be outlier==<br />
[http://www.huffingtonpost.com/2012/06/20/bloomberg-poll-barack-obama-lead_n_1612758.html?utm_source=Triggermail&utm_medium=email&utm_term=Daily%20Brief&utm_campaign=daily_brief “Bloomberg Poll Shows Big But Questionable Obama Lead”]<br><br />
Huffington Post, June 20, 2012 <br><br />
<br />
A Bloomberg News national poll shows Obama leading his Republican challenger by a “surprisingly large margin of 53 to 40 percent,” instead of the (at most) single-digit margin shown in other recent polls.<br> <br />
<br />
While a Bloomberg representative expressed the same surprise as others, she stated that this result is based on a sample with the same demographics as its previous polls and on its usual methodology.<br> <br />
<br />
The article’s author states:<br />
<blockquote> The most likely possibility is that this poll simply represents a statistical outlier. Yes, with a 3 percent margin of error, its Obama advantage of 53 to 40 percent is significantly different than the low single-digit lead suggested by the polling averages. However, that margin of error assumes a 95 percent level of confidence, which in simpler language means that one poll estimate in 20 will fall outside the margin of error by chance alone.</blockquote><br />
See Bloomberg’s report about the poll [http://www.bloomberg.com/news/2012-06-20/obama-leads-in-poll-as-voters-view-romney-as-out-of-touch.html here].<br><br />
<br />
Submitted by Margaret Cibes<br />
<br />
===Further discussion from FiveThirtyEight===<br />
[http://fivethirtyeight.blogs.nytimes.com/2012/06/20/outlier-polls-are-no-substitute-for-news/ Outlier polls are no substitute for news]<br><br />
by Nate Silver, FiveThirtyEight blog, ''New York Times'', 20 June 2012<br />
<br />
Silver identifies two options for dealing with such a poll, which a number of news sources have describe as an "outlier." One could simply choose to disregard it, or else "include it in some sort of average and then get on with your life." He with the following excellent advice:<br />
<blockquote><br />
My general view...is that you should not throw out data without a good reason. If cherry-picking the two or three data points that you like the most is a sin of the first order, disregarding the two or three data points that you like the least will lead to many of the same problems.<br />
</blockquote><br />
<br />
In the case of the Bloomberg poll, because the organization has a good record on accuracy, he has chosen to include it the overall average of poll results that he uses for FiveThirtyEight forecasts.<br />
<br />
Description of one of the further adjustments that Silver makes in his model can be found in his later post [http://fivethirtyeight.blogs.nytimes.com/2012/06/22/calculating-house-effects-of-polling-firms/ Calculating ‘house effects’ of polling firms] (22 June 2012). Silver explains that what often is interpreted as movement in public opinion as measured in two different polls can instead be a reflection of systematic tendencies of polling organizations to favor either Democratic or Republican candidates. Reproduced below is a chart from the post that shows the size and direction of this house effect for some major organizations:<br />
<br />
<center>[[File:Fivethirtyeight-poll-bias.png]]</center><br />
<br />
As described there, "The house effect adjustment is calculated by applying a regression analysis that compares the results of different polling firms’ surveys in the same states...The regression analysis makes these comparisons across all combinations of polling firms and states, and comes up with an overall estimate of the house effect as a result." Looking at the table, it is interesting to note that these effects are comparable to the stated margin of sampling error for typical national polls.<br />
<br />
Submitted by Bill Peterson<br />
<br />
==Rock-paper-scissors in Texas elections==<br />
[http://online.wsj.com/article/SB10001424052702303703004577476361108859928.html?KEYWORDS=nathan+koppel “Elections are a Crap Shoot in Texas, Where a Roll of the Dice Can Win”]<br><br />
by Nathan Koppel, <i>The Wall Street Journal</i>, June 19, 2012<br><br />
<br />
The state of Texas permits tied candidates to agree to “settle the matter by a game of chance.” The article describes instances of candidates using a die or a coin to decide an election.<br><br />
<br />
In one case, “leaving nothing to chance, the city attorney drafted a three-page agreement ahead of time detailing how the flip would be conducted.”<br><br />
<br />
However, not any game is permitted:<br />
<blockquote>Tonya Roberts, city secretary for Rice … consulted the Texas secretary of state's office after a city-council race ended last month in a 25-25 tie. She asked whether the race could be settled with a game of "rock, paper, scissors" but was told no. "I guess some people do consider that a game of skill," she said.</blockquote><br />
For some suggested strategies for winning this game, see [http://www.wikihow.com/Win-at-Rock,-Paper,-Scissors “How to Win at Rock, Paper, Scissors”] in wikiHow, and/or [http://blogs.discovermagazine.com/notrocketscience/2011/07/19/to-win-at-rock-paper-scissors-put-on-a-blindfold/ “To win at rock-paper-scissors, put on a blindfold”], in Discover Magazine.<br />
<br />
===Discussion===<br />
Assume that the use of the rock-paper-scissors game had <i>not</i> been suggested by one of the Rice candidates, who might have been an experienced player. Do you think that a one-time play of this game, between random strangers, could have been considered a game of chance? Why or why not?<br><br />
<br />
Submitted by Margaret Cibes<br />
<br />
==NSF may stop funding a “soft” science==<br />
[http://www.nytimes.com/2012/06/24/opinion/sunday/political-scientists-are-lousy-forecasters.html?_r=1&pagewanted=all “Political Scientists Are Lousy Forecasters”]<br><br />
by Jacqueline Stevens, <i>The New York Times</i>, June 23, 2012<br><br />
<br />
A Northwestern University political science professor has written an op-ed piece responding to a House-passed amendment that would eliminate NSF grants to political scientists. To date the Senate has not voted on the bill.<br><br />
<br />
She provides several anecdotes about political scientists having made incorrect predictions and states that she is “sympathetic with the [group] behind this amendment.” She feels that:<br />
<blockquote>[T]he government — disproportionately — supports research that is amenable to statistical analyses and models even though everyone knows the clean equations mask messy realities that contrived data sets and assumptions don’t, and can’t, capture. …. It’s an open secret in my discipline: in terms of accurate political predictions …, my colleagues have failed spectacularly and wasted colossal amounts of time and money. …. Many of today’s peer-reviewed studies offer trivial confirmations of the obvious and policy documents filled with egregious, dangerous errors. ….I look forward to seeing what happens to my discipline and politics more generally once we stop mistaking probability studies and statistical significance for knowledge.</blockquote><br />
===Discussion===<br />
1. The author makes a number of categorical statements based on anecdotal evidence. Could her conclusions about political science research be an example of the [http://en.wikipedia.org/wiki/Availability_heuristic “availability heuristic/fallacy”]?<br><br />
2. Do you think that the problems the author identifies are limited to, or at least more common in, the area of political science than in the other "soft," or even any "hard," sciences? What information would you need in order to confirm/reject your opinion?<br><br />
<br />
(Disclosure: The submitter's spouse is a political scientist, whose Ph.D. program, including stats, was entirely funded by a government act (National Defense Education Act), but who is also skeptical about <i>some</i> social science research.)<br />
<br />
Submitted by Margaret Cibes at the suggestion of James Greenwood<br />
<br />
==Crowdsourcing and its failed prediction on health care law==<br />
<br />
[http://www.nytimes.com/2012/07/08/sunday-review/when-the-crowd-isnt-wise.html When the Crowd Isn't Wise] by David Leonhardt, The New York Times, July 7, 2012.<br />
<br />
Many people were surprised at the U.S. Supreme Court ruling that upheld most aspects of the Affordable Care Act (ACA). That included a prominent source that relied on crowdsoucing. Intrade, an online prediction market. Intrade results indicated that the individual insurance mandate would be ruled unconstitutional. This prediction held steady in spite of some last minute rumors about the Supreme Court decision.<br />
<br />
<blockquote>With the rumors swirling, I began to check the odds at Intrade, the online prediction market where people can bet on real-world events, several times a day. The odds had barely budged. They continued to show about a 75 percent chance that the law’s so-called mandate would be ruled unconstitutional, right up until the morning it was ruled constitutional.</blockquote><br />
<br />
The concept of crowdsourcing has been [http://test.causeweb.org/wiki/chance/index.php/Chance_News_15#The_future_divined_by_the_crowd discussed on Chance News] before. The basic idea is that individual experts have systematic biases, but these biases cancel out when averaged over a large number of people.<br />
<br />
Crowdsourcing does have some notable successes, but it isn't perfect.<br />
<br />
<blockquote>For one thing, many of the betting pools on Intrade and Betfair attract relatively few traders, in part because using them legally is cumbersome. (No, I do not know from experience.) The thinness of these markets can cause them to adjust too slowly to new information.</blockquote><br />
<br />
<blockquote>And there is this: If the circle of people who possess information is small enough — as with the selection of a vice president or pope or, arguably, a decision by the Supreme Court — the crowds may not have much wisdom to impart. “There is a class of markets that I think are basically pointless,” says Justin Wolfers, an economist whose research on prediction markets, much of it with Eric Zitzewitz of Dartmouth, has made him mostly a fan of them. “There is no widely available public information.”</blockquote><br />
<br />
So, should you return to the individual expert for prediction? Maybe not.<br />
<br />
<blockquote>Mutual fund managers, as a class, lose their clients’ money because they do not outperform the market and charge fees for their mediocrity. Sports pundits have a dismal record of predicting games relative to the Las Vegas odds, which are just another market price. As imperfect as prediction markets are in forecasting elections, they have at least as good a recent record as polls. Or consider the housing bubble: both the market and most experts missed it. </blockquote><br />
<br />
Mr. Leonhardt offers a middle path.<br />
<br />
<blockquote>The answer, I think, is to take the best of what both experts and markets have to offer, realizing that the combination of the two offers a better window onto the future than either alone. Markets are at their best when they can synthesize large amounts of disparate information, as on an election night. Experts are most useful when a system exists to identify the most truly knowledgeable — a system that often resembles a market.</blockquote><br />
<br />
This last sentence introduces the thought that you use crowdsourcing to find the best experts. Social media like Twitter allows people an interesting way to identify experts who are truly experts.<br />
<br />
<blockquote>Think for a moment about what a Twitter feed is: it’s a personalized market of experts (and friends), in which you can build your own focus group and listen to its collective analysis about the past, present and future. An RSS feed, in which you choose blogs to read, works similarly. You make decisions about which experts are worthy of your attention, based both on your own judgments about them and on other experts’ judgments.</blockquote><br />
<br />
<blockquote>Their predictions now face a market discipline that did not always exist before the Internet came along. “Experts exist,” as Mr. Wolfers says, “but they’re not necessarily the same as the guys on TV.” </blockquote><br />
<br />
===Questions===<br />
<br />
1. How bad did Intrade really perform on the Supreme Court decision on ACA? How large a probability does a system like Intrade have to place on a bad prediction to cause you to lose faith in it?<br />
<br />
2. What are some of the potential problems with identifying experts by the number of retweets that they get in Twitter?</div>Simon66217https://www.causeweb.org/wiki/chance/index.php?title=Chance_News_86&diff=16135Chance News 862012-07-09T18:43:39Z<p>Simon66217: /* Crowdsourcing and its failed prediction on health care law */</p>
<hr />
<div>==Quotations==<br />
"Asymptotically we are all dead."<br />
<div align=right>--paraphrase of Keynes, sometimes attributed to Melvin R. Novick</div><br />
<br />
Submitted by Paul Alper<br />
<br />
----<br />
"To err is human, to forgive divine but to include errors in your design is statistical."<br />
<div align=right>--Leslie Kish, in [http://asapresidentialpapers.info/documents/Kish_Leslie_1977_edit_(wla_092809).pdf Chance, statistics, and statisticians], 1977 ASA Presidential Address</div><br />
<br />
Submitted by Bill Peterson<br />
<br />
----<br />
"The only statistical test one ever needs is the IOTT or 'interocular trauma test.' The result just hits one between the eyes. If one needs any more statistical analysis, one should be working harder to control sources of error, or perhaps studying something else entirely."<br />
<br />
<div align=right>--David H. Krantz, in [http://www.unt.edu/rss/class/mike/5030/articles/krantznhst.pdf The null hypothesis testing in psychology], ''JASA'', December 1999, p. 1373.</div><br />
<br />
Krantz is describing how some psychologists view statistical testing. On the same page he describes another viewpoint:<br />
<br />
:"Nothing is due to chance. This is the Freudian stance...but fortunately, it has little support among researchers."<br />
<br />
Submitted by Paul Alper<br />
<br />
==Forsooth==<br />
“The survey interviewed 991 Americans online from June 28-30. The precision of the Reuters/Ipsos online polls is measured using a <i>credibility</i> interval. In this case, the poll has a <i>credibility</i> interval of plus or minus 3.6 percentage points.” (emphasis added)<br />
<div align=right>[http://www.huffingtonpost.com/2012/07/01/obamacare-supreme-court-ruling_n_1641560.html?ref=topbar “Obamacare Support Rises After Supreme Court Ruling, Poll Finds”]<br><br />
<i>Huffington Post</i>, July 1, 2012</div><br />
<br />
Submitted by Margaret Cibes<br />
<br />
==Gaydar==<br />
[http://www.nytimes.com/2012/06/03/opinion/sunday/the-science-of-gaydar.html The science of ‘gaydar’]<br><br />
by Joshua A. Tabak and Vivian Zayas, ''New York Times'', 3 June 2012<br />
<br />
The definition of GAYDAR is the "Ability to sense a homosexual" according to [http://www.internetslang.com/GAYDAR-meaning-definition.asp internetslang.com.] <br />
<br />
In their NYT article, Tabak and Zayas write<br />
<blockquote><br />
Should you trust your gaydar in everyday life? Probably not. In our experiments, average gaydar judgment accuracy was only in the 60 percent range. This demonstrates gaydar ability — which is far from judgment proficiency. But is gaydar real? Absolutely. <br />
</blockquote><br />
At [http://www.plosone.org/article/info%3Adoi%2F10.1371%2Fjournal.pone.0036671 PLoS One] is their complete research paper where subjects viewed facial photographs of homosexuals and straights; the subjects then had a short time to decide the sexual orientation. In their first experiment, the faces were shown upright only:<br />
<blockquote><br />
Twenty-four University of Washington students (19 women; age range = 18–22 years) participated in exchange for extra course credit. Data from seven additional participants were excluded from analyses due to failure to follow instructions (n = 4) or computer malfunction (n = 3). <br />
</blockquote><br />
<br />
In the second experiment, faces were shown upright and upside-down and the subjects had a short time to decide the sexual orientation:<br />
<blockquote><br />
One hundred twenty-nine University of Washington students (92 women; age range = 18–25 years) participated in exchange for extra course credit. Data from 16 additional participants were excluded from analyses due to failure to follow instructions (n = 12) or average reaction times more than 3 SD above the mean (n = 4). </blockquote><br />
<br />
According to the authors, “there are two components of “accuracy”: the hit rate which is “the proportion of gay faces correctly perceived as gay, and the false alarm rate” which is “the proportion of straight faces incorrectly perceived as gay.” The figure reproduced below (full version [http://www.plosone.org/article/slideshow.action?uri=info:doi/10.1371/journal.pone.0036671&imageURI=info:doi/10.1371/journal.pone.0036671.g003 here]) indicates that the accuracy was better when the target gender was women as opposed to men and the accuracy was better when the faces were upright as opposed to upside down. Presumably, random guessing would produce an “accuracy” of about .5.<br />
<br />
::[[File:TabakZayas_fig3.png ]]<br />
<br />
::Figure 3. Accuracy of detecting sexual orientation from upright and upside-down faces (Experiment 2).<br />
::Mean accuracy (A′) in judging sexual orientation from faces presented for 50 milliseconds as a function of the target’s gender and spatial orientation (upright or upside-down; Experiment 2). Judgments of upright faces are based on both configural and featural processing, whereas judgments of upside-down faces are based only on featural face processing. Error bars represent ±1 SEM.<br />
<br />
Reproduced below are the detailed results for the second experiment:<br />
::[[File:TabakZayas_table1.png]]<br />
<br />
::Table 1. Hit and False Alarm Rates for Snap Judgments of Sexual Orientation in Experiment 2.<br />
<br />
The author’s conclude with the following statement:<br />
<blockquote><br />
<br />
The present research is the first to demonstrate (a) that configural face processing significantly contributes to perception of sexual orientation, and (b) that sexual orientation is inferred more easily from women’s vs. men’s faces. In light of these findings, it is interesting to note the popular desire to learn to read faces like books. Considering how challenging it is to read a book upside-down, it seems that we read faces better than we read books.<br />
</blockquote><br />
<br />
===Discussion===<br />
<br />
1. Gaydar "accuracy" seems to be defined in the paper as hit rate / (hit rate + false alarm rate) or, to use terms common in [http://en.wikipedia.org/wiki/Sensitivity_and_specificity medical tests,] positive predictive value = true positive / (true positive + false positive). The paper makes no mention of negative predictive value = true negative / (true negative + false negative). As is illustrated in the Wikipedia article, legitimate medical tests will tolerate a low positive predictive value because a more expensive test exists in the rare case that the disease is actually present; negative predictive values must be high to avoid potentially deadly false optimism. The situation here is somewhat different because the subjects were exposed to an approximately equal number of gays and straights whereas in medical tests, most people in the population do not have the “disease.”<br />
<br />
2. Perhaps the analogy with medical testing is inappropriate. That is, an error is an error and no distinction should be made between the two types of errors. Consider the above table for the case of women and upright spatial orientation. The hit rate is .36 and the false alarm rate is .22. If we assume that the 67 subjects viewed 100 gay faces and 100 straight faces, then we obtain the following table for average values:<br />
<br />
<center><br />
{| class="wikitable" style="text-align:center; width:50%;"<br />
|-<br />
! scope="col" | Orientation<br />
! scope="col" | +<br />
! scope="col" | -<br />
! scope="col" | Total<br />
|-<br />
! scope="row" | Gay<br />
| 36<br />
| 64<br />
| 100<br />
|-<br />
! scope="row" | Straight<br />
| 22<br />
| 78<br />
| 100<br />
|-<br />
! scope="row" | Total<br />
| 58<br />
| 142<br />
| 200<br />
|}<br />
</center><br />
This leads to Prob (success) = (36 + 78)/200 = .57; Prob (error) = (64 +22)/200 = .43<br />
In effect, the model could be hidden tosses of a coin and the subjects, in an ESP fashion, guess heads or tails before the toss. Of course, a Bayesian would then assume a prior distribution and combine that with the results of the study to obtain a posterior probability and avoid any mention of p-value based on .5 as the null.<br />
<br />
3. Why might the following be a useful way of assessing gaydar via a modification of the procedure used in the article? Repeat the second experiment with no gays.<br />
<br />
4. Why might the following be a useful way of assessing gaydar via a modification of the procedure used in the article? Repeat the second experiment with no straights.<br />
<br />
5. In case the reader feels that Discussion #3 and #4 are deceptive, see<br />
[http://www.psychwiki.com/wiki/Deception_(methodological_technique) this Psychwiki] which looks at the history of deception in social psychology:<br />
<blockquote><br />
deception can often be seen in the “cover story” for the study, which provides the participant with a justification for the procedures and measures used. The ultimate goal of using deception in research is to ensure that the behaviors or reactions observed in a controlled laboratory setting are as close to those behaviors and reactions that occur outside of the laboratory setting. </blockquote><br />
<br />
Submitted by Paul Alper<br />
<br />
==Coin experiments==<br />
[http://online.wsj.com/article/SB10001424052702303753904577454431281272936.html?KEYWORDS=boese “The Pleasures of Suffering for Science”], <i>The Wall Street Journal</i>, June 8, 2012<br><br />
<br />
This story, about scientists experimenting on themselves, included a reference to a coin-tossing experiment:<br />
<blockquote>Even mathematics offers an example of physical self-sacrifice, through repetitive stress injury. University of Georgia professor Pope R. Hill flipped a coin 100,000 times to prove that heads and tails would come up an approximately equal number of times. The experiment lasted a year. He fell sick but completed the count, though he had to enlist the aid of an assistant near the end.</blockquote><br />
A Google search for Prof. Hill turned up the following story at the [http://www.weirduniverse.net/blog/comments/testing_the_law_of_probability “Weird Science”] website:<br />
<blockquote> If you repeatedly flip a coin, the law of probability states that approximately half the time you should get heads and half the time tails. But does this law hold true in practice?<br> <br />
Pope R. Hill, a professor at the University of Georgia during the 1930s, wanted to find out. But he thought coin-flipping was too imprecise a measurement, since any one coin might be imbalanced, causing it to favor heads or tails.<br><br />
Instead, he filled a can with 200 pennies. Half were dated 1919, half dated 1920. He shook up the can, withdrew a coin, and recorded its date. Then he returned the coin to the can. He repeated this procedure 100,000 times!<br><br />
Of the 100,000 draws, 50,145 came out 1920. 49,855 came out 1919. Hill concluded that the law of half and half does work out in practice.</blockquote><br />
<br />
===Discussion===<br />
1. Do you think that drawing a single coin from among 1919 and 1920 coins - even in a perfectly shaken can - would solve the problem of potential imbalance between heads and tails on any single coin toss? Can you think of any possible imbalance in the former case?<br><br />
2. In the second story, there is a remarkable relationship between Hill’s final counts. What questions, if any, might it raise in your mind about the experiment?<br><br />
3. Which is the more accurate expectation from a coin-tossing experiment: (a) “heads and tails would come up an approximately equal number of times” (first story) or (b) “approximately half the time you should get heads and half the time tails” (second story)?<br><br />
<br />
Submitted by Margaret Cibes<br />
<br />
'''Note''': In the [http://www.dartmouth.edu/~chance/chance_news/recent_news/chance_news_11.01.html#item8 archives of the Chance Newsletter] there is a description of some other historical attempts to empirically demonstrate the chance of heads. We read there:<br />
<blockquote>The French naturalist Count Buffon (1707-1788), know to us from the Buffon needle problem, tossed a coin 4040 times with heads coming up 2048 or 50.693 percent of the time. Karl Pearson tossed a coin 24,000 times with head coming up 12,012 or 50.05 percent of the time. While imprisoned by the Germans in the second world war, South African mathematician John Kerrich tossed a coin 10,000 times with heads coming up 5067 or 50.67 percent of the time. You can find his data in the classic text, ''Statistics'', by Freedman, Pisani and Purves.</blockquote><br />
<br />
==New presidential poll may be outlier==<br />
[http://www.huffingtonpost.com/2012/06/20/bloomberg-poll-barack-obama-lead_n_1612758.html?utm_source=Triggermail&utm_medium=email&utm_term=Daily%20Brief&utm_campaign=daily_brief “Bloomberg Poll Shows Big But Questionable Obama Lead”]<br><br />
Huffington Post, June 20, 2012 <br><br />
<br />
A Bloomberg News national poll shows Obama leading his Republican challenger by a “surprisingly large margin of 53 to 40 percent,” instead of the (at most) single-digit margin shown in other recent polls.<br> <br />
<br />
While a Bloomberg representative expressed the same surprise as others, she stated that this result is based on a sample with the same demographics as its previous polls and on its usual methodology.<br> <br />
<br />
The article’s author states:<br />
<blockquote> The most likely possibility is that this poll simply represents a statistical outlier. Yes, with a 3 percent margin of error, its Obama advantage of 53 to 40 percent is significantly different than the low single-digit lead suggested by the polling averages. However, that margin of error assumes a 95 percent level of confidence, which in simpler language means that one poll estimate in 20 will fall outside the margin of error by chance alone.</blockquote><br />
See Bloomberg’s report about the poll [http://www.bloomberg.com/news/2012-06-20/obama-leads-in-poll-as-voters-view-romney-as-out-of-touch.html here].<br><br />
<br />
Submitted by Margaret Cibes<br />
<br />
===Further discussion from FiveThirtyEight===<br />
[http://fivethirtyeight.blogs.nytimes.com/2012/06/20/outlier-polls-are-no-substitute-for-news/ Outlier polls are no substitute for news]<br><br />
by Nate Silver, FiveThirtyEight blog, ''New York Times'', 20 June 2012<br />
<br />
Silver identifies two options for dealing with such a poll, which a number of news sources have describe as an "outlier." One could simply choose to disregard it, or else "include it in some sort of average and then get on with your life." He with the following excellent advice:<br />
<blockquote><br />
My general view...is that you should not throw out data without a good reason. If cherry-picking the two or three data points that you like the most is a sin of the first order, disregarding the two or three data points that you like the least will lead to many of the same problems.<br />
</blockquote><br />
<br />
In the case of the Bloomberg poll, because the organization has a good record on accuracy, he has chosen to include it the overall average of poll results that he uses for FiveThirtyEight forecasts.<br />
<br />
Description of one of the further adjustments that Silver makes in his model can be found in his later post [http://fivethirtyeight.blogs.nytimes.com/2012/06/22/calculating-house-effects-of-polling-firms/ Calculating ‘house effects’ of polling firms] (22 June 2012). Silver explains that what often is interpreted as movement in public opinion as measured in two different polls can instead be a reflection of systematic tendencies of polling organizations to favor either Democratic or Republican candidates. Reproduced below is a chart from the post that shows the size and direction of this house effect for some major organizations:<br />
<br />
<center>[[File:Fivethirtyeight-poll-bias.png]]</center><br />
<br />
As described there, "The house effect adjustment is calculated by applying a regression analysis that compares the results of different polling firms’ surveys in the same states...The regression analysis makes these comparisons across all combinations of polling firms and states, and comes up with an overall estimate of the house effect as a result." Looking at the table, it is interesting to note that these effects are comparable to the stated margin of sampling error for typical national polls.<br />
<br />
Submitted by Bill Peterson<br />
<br />
==Rock-paper-scissors in Texas elections==<br />
[http://online.wsj.com/article/SB10001424052702303703004577476361108859928.html?KEYWORDS=nathan+koppel “Elections are a Crap Shoot in Texas, Where a Roll of the Dice Can Win”]<br><br />
by Nathan Koppel, <i>The Wall Street Journal</i>, June 19, 2012<br><br />
<br />
The state of Texas permits tied candidates to agree to “settle the matter by a game of chance.” The article describes instances of candidates using a die or a coin to decide an election.<br><br />
<br />
In one case, “leaving nothing to chance, the city attorney drafted a three-page agreement ahead of time detailing how the flip would be conducted.”<br><br />
<br />
However, not any game is permitted:<br />
<blockquote>Tonya Roberts, city secretary for Rice … consulted the Texas secretary of state's office after a city-council race ended last month in a 25-25 tie. She asked whether the race could be settled with a game of "rock, paper, scissors" but was told no. "I guess some people do consider that a game of skill," she said.</blockquote><br />
For some suggested strategies for winning this game, see [http://www.wikihow.com/Win-at-Rock,-Paper,-Scissors “How to Win at Rock, Paper, Scissors”] in wikiHow, and/or [http://blogs.discovermagazine.com/notrocketscience/2011/07/19/to-win-at-rock-paper-scissors-put-on-a-blindfold/ “To win at rock-paper-scissors, put on a blindfold”], in Discover Magazine.<br />
<br />
===Discussion===<br />
Assume that the use of the rock-paper-scissors game had <i>not</i> been suggested by one of the Rice candidates, who might have been an experienced player. Do you think that a one-time play of this game, between random strangers, could have been considered a game of chance? Why or why not?<br><br />
<br />
Submitted by Margaret Cibes<br />
<br />
==NSF may stop funding a “soft” science==<br />
[http://www.nytimes.com/2012/06/24/opinion/sunday/political-scientists-are-lousy-forecasters.html?_r=1&pagewanted=all “Political Scientists Are Lousy Forecasters”]<br><br />
by Jacqueline Stevens, <i>The New York Times</i>, June 23, 2012<br><br />
<br />
A Northwestern University political science professor has written an op-ed piece responding to a House-passed amendment that would eliminate NSF grants to political scientists. To date the Senate has not voted on the bill.<br><br />
<br />
She provides several anecdotes about political scientists having made incorrect predictions and states that she is “sympathetic with the [group] behind this amendment.” She feels that:<br />
<blockquote>[T]he government — disproportionately — supports research that is amenable to statistical analyses and models even though everyone knows the clean equations mask messy realities that contrived data sets and assumptions don’t, and can’t, capture. …. It’s an open secret in my discipline: in terms of accurate political predictions …, my colleagues have failed spectacularly and wasted colossal amounts of time and money. …. Many of today’s peer-reviewed studies offer trivial confirmations of the obvious and policy documents filled with egregious, dangerous errors. ….I look forward to seeing what happens to my discipline and politics more generally once we stop mistaking probability studies and statistical significance for knowledge.</blockquote><br />
===Discussion===<br />
1. The author makes a number of categorical statements based on anecdotal evidence. Could her conclusions about political science research be an example of the [http://en.wikipedia.org/wiki/Availability_heuristic “availability heuristic/fallacy”]?<br><br />
2. Do you think that the problems the author identifies are limited to, or at least more common in, the area of political science than in the other "soft," or even any "hard," sciences? What information would you need in order to confirm/reject your opinion?<br><br />
<br />
(Disclosure: The submitter's spouse is a political scientist, whose Ph.D. program, including stats, was entirely funded by a government act (National Defense Education Act), but who is also skeptical about <i>some</i> social science research.)<br />
<br />
Submitted by Margaret Cibes at the suggestion of James Greenwood<br />
<br />
==Crowdsourcing and its failed prediction on health care law==<br />
<br />
[http://www.nytimes.com/2012/07/08/sunday-review/when-the-crowd-isnt-wise.html When the Crowd Isn't Wise] by David Leonhardt, The New York Times, July 7, 2012.<br />
<br />
Many people were surprised at the U.S. Supreme Court ruling that upheld most aspects of the Affordable Care Act (ACA). That included a prominent source that relied on crowdsoucing. Intrade, an online prediction market. Intrade results indicated that the individual insurance mandate would be ruled unconstitutional. This prediction held steady in spite of some last minute rumors about the Supreme Court decision.<br />
<br />
<blockquote>With the rumors swirling, I began to check the odds at Intrade, the online prediction market where people can bet on real-world events, several times a day. The odds had barely budged. They continued to show about a 75 percent chance that the law’s so-called mandate would be ruled unconstitutional, right up until the morning it was ruled constitutional.</blockquote><br />
<br />
The concept of crowdsourcing has been [http://test.causeweb.org/wiki/chance/index.php/Chance_News_15#The_future_divined_by_the_crowd discussed on Chance News] before. The basic concept is that individual experts have systematic biases, but these biases cancel out when averaged over a large number of people.<br />
<br />
Crowdsourcing does have some notable successes, but it isn't perfect.<br />
<br />
<blockquote>For one thing, many of the betting pools on Intrade and Betfair attract relatively few traders, in part because using them legally is cumbersome. (No, I do not know from experience.) The thinness of these markets can cause them to adjust too slowly to new information.</blockquote><br />
<br />
<blockquote>And there is this: If the circle of people who possess information is small enough — as with the selection of a vice president or pope or, arguably, a decision by the Supreme Court — the crowds may not have much wisdom to impart. “There is a class of markets that I think are basically pointless,” says Justin Wolfers, an economist whose research on prediction markets, much of it with Eric Zitzewitz of Dartmouth, has made him mostly a fan of them. “There is no widely available public information.”</blockquote><br />
<br />
So, should you return to the individual expert for prediction? Maybe not.<br />
<br />
<blockquote>Mutual fund managers, as a class, lose their clients’ money because they do not outperform the market and charge fees for their mediocrity. Sports pundits have a dismal record of predicting games relative to the Las Vegas odds, which are just another market price. As imperfect as prediction markets are in forecasting elections, they have at least as good a recent record as polls. Or consider the housing bubble: both the market and most experts missed it. </blockquote><br />
<br />
Mr. Leonhardt offers a middle path.<br />
<br />
<blockquote>The answer, I think, is to take the best of what both experts and markets have to offer, realizing that the combination of the two offers a better window onto the future than either alone. Markets are at their best when they can synthesize large amounts of disparate information, as on an election night. Experts are most useful when a system exists to identify the most truly knowledgeable — a system that often resembles a market.</blockquote><br />
<br />
This last sentence introduces the thought that you use crowdsourcing to find the best experts. Social media like Twitter allows people an interesting way to identify experts who are truly experts.<br />
<br />
<blockquote>Think for a moment about what a Twitter feed is: it’s a personalized market of experts (and friends), in which you can build your own focus group and listen to its collective analysis about the past, present and future. An RSS feed, in which you choose blogs to read, works similarly. You make decisions about which experts are worthy of your attention, based both on your own judgments about them and on other experts’ judgments.</blockquote><br />
<br />
<blockquote>Their predictions now face a market discipline that did not always exist before the Internet came along. “Experts exist,” as Mr. Wolfers says, “but they’re not necessarily the same as the guys on TV.” </blockquote><br />
<br />
===Questions===<br />
<br />
1. How bad did Intrade really perform on the Supreme Court decision on ACA? How large a probability does a system like Intrade have to place on a bad prediction to cause you to lose faith in it?<br />
<br />
2. What are some of the potential problems with identifying experts by the number of retweets that they get in Twitter?</div>Simon66217https://www.causeweb.org/wiki/chance/index.php?title=Chance_News_86&diff=16134Chance News 862012-07-09T18:41:06Z<p>Simon66217: /* Crowdsourcing and its failed prediction on health care law */</p>
<hr />
<div>==Quotations==<br />
"Asymptotically we are all dead."<br />
<div align=right>--paraphrase of Keynes, sometimes attributed to Melvin R. Novick</div><br />
<br />
Submitted by Paul Alper<br />
<br />
----<br />
"To err is human, to forgive divine but to include errors in your design is statistical."<br />
<div align=right>--Leslie Kish, in [http://asapresidentialpapers.info/documents/Kish_Leslie_1977_edit_(wla_092809).pdf Chance, statistics, and statisticians], 1977 ASA Presidential Address</div><br />
<br />
Submitted by Bill Peterson<br />
<br />
----<br />
"The only statistical test one ever needs is the IOTT or 'interocular trauma test.' The result just hits one between the eyes. If one needs any more statistical analysis, one should be working harder to control sources of error, or perhaps studying something else entirely."<br />
<br />
<div align=right>--David H. Krantz, in [http://www.unt.edu/rss/class/mike/5030/articles/krantznhst.pdf The null hypothesis testing in psychology], ''JASA'', December 1999, p. 1373.</div><br />
<br />
Krantz is describing how some psychologists view statistical testing. On the same page he describes another viewpoint:<br />
<br />
:"Nothing is due to chance. This is the Freudian stance...but fortunately, it has little support among researchers."<br />
<br />
Submitted by Paul Alper<br />
<br />
==Forsooth==<br />
“The survey interviewed 991 Americans online from June 28-30. The precision of the Reuters/Ipsos online polls is measured using a <i>credibility</i> interval. In this case, the poll has a <i>credibility</i> interval of plus or minus 3.6 percentage points.” (emphasis added)<br />
<div align=right>[http://www.huffingtonpost.com/2012/07/01/obamacare-supreme-court-ruling_n_1641560.html?ref=topbar “Obamacare Support Rises After Supreme Court Ruling, Poll Finds”]<br><br />
<i>Huffington Post</i>, July 1, 2012</div><br />
<br />
Submitted by Margaret Cibes<br />
<br />
==Gaydar==<br />
[http://www.nytimes.com/2012/06/03/opinion/sunday/the-science-of-gaydar.html The science of ‘gaydar’]<br><br />
by Joshua A. Tabak and Vivian Zayas, ''New York Times'', 3 June 2012<br />
<br />
The definition of GAYDAR is the "Ability to sense a homosexual" according to [http://www.internetslang.com/GAYDAR-meaning-definition.asp internetslang.com.] <br />
<br />
In their NYT article, Tabak and Zayas write<br />
<blockquote><br />
Should you trust your gaydar in everyday life? Probably not. In our experiments, average gaydar judgment accuracy was only in the 60 percent range. This demonstrates gaydar ability — which is far from judgment proficiency. But is gaydar real? Absolutely. <br />
</blockquote><br />
At [http://www.plosone.org/article/info%3Adoi%2F10.1371%2Fjournal.pone.0036671 PLoS One] is their complete research paper where subjects viewed facial photographs of homosexuals and straights; the subjects then had a short time to decide the sexual orientation. In their first experiment, the faces were shown upright only:<br />
<blockquote><br />
Twenty-four University of Washington students (19 women; age range = 18–22 years) participated in exchange for extra course credit. Data from seven additional participants were excluded from analyses due to failure to follow instructions (n = 4) or computer malfunction (n = 3). <br />
</blockquote><br />
<br />
In the second experiment, faces were shown upright and upside-down and the subjects had a short time to decide the sexual orientation:<br />
<blockquote><br />
One hundred twenty-nine University of Washington students (92 women; age range = 18–25 years) participated in exchange for extra course credit. Data from 16 additional participants were excluded from analyses due to failure to follow instructions (n = 12) or average reaction times more than 3 SD above the mean (n = 4). </blockquote><br />
<br />
According to the authors, “there are two components of “accuracy”: the hit rate which is “the proportion of gay faces correctly perceived as gay, and the false alarm rate” which is “the proportion of straight faces incorrectly perceived as gay.” The figure reproduced below (full version [http://www.plosone.org/article/slideshow.action?uri=info:doi/10.1371/journal.pone.0036671&imageURI=info:doi/10.1371/journal.pone.0036671.g003 here]) indicates that the accuracy was better when the target gender was women as opposed to men and the accuracy was better when the faces were upright as opposed to upside down. Presumably, random guessing would produce an “accuracy” of about .5.<br />
<br />
::[[File:TabakZayas_fig3.png ]]<br />
<br />
::Figure 3. Accuracy of detecting sexual orientation from upright and upside-down faces (Experiment 2).<br />
::Mean accuracy (A′) in judging sexual orientation from faces presented for 50 milliseconds as a function of the target’s gender and spatial orientation (upright or upside-down; Experiment 2). Judgments of upright faces are based on both configural and featural processing, whereas judgments of upside-down faces are based only on featural face processing. Error bars represent ±1 SEM.<br />
<br />
Reproduced below are the detailed results for the second experiment:<br />
::[[File:TabakZayas_table1.png]]<br />
<br />
::Table 1. Hit and False Alarm Rates for Snap Judgments of Sexual Orientation in Experiment 2.<br />
<br />
The author’s conclude with the following statement:<br />
<blockquote><br />
<br />
The present research is the first to demonstrate (a) that configural face processing significantly contributes to perception of sexual orientation, and (b) that sexual orientation is inferred more easily from women’s vs. men’s faces. In light of these findings, it is interesting to note the popular desire to learn to read faces like books. Considering how challenging it is to read a book upside-down, it seems that we read faces better than we read books.<br />
</blockquote><br />
<br />
===Discussion===<br />
<br />
1. Gaydar "accuracy" seems to be defined in the paper as hit rate / (hit rate + false alarm rate) or, to use terms common in [http://en.wikipedia.org/wiki/Sensitivity_and_specificity medical tests,] positive predictive value = true positive / (true positive + false positive). The paper makes no mention of negative predictive value = true negative / (true negative + false negative). As is illustrated in the Wikipedia article, legitimate medical tests will tolerate a low positive predictive value because a more expensive test exists in the rare case that the disease is actually present; negative predictive values must be high to avoid potentially deadly false optimism. The situation here is somewhat different because the subjects were exposed to an approximately equal number of gays and straights whereas in medical tests, most people in the population do not have the “disease.”<br />
<br />
2. Perhaps the analogy with medical testing is inappropriate. That is, an error is an error and no distinction should be made between the two types of errors. Consider the above table for the case of women and upright spatial orientation. The hit rate is .36 and the false alarm rate is .22. If we assume that the 67 subjects viewed 100 gay faces and 100 straight faces, then we obtain the following table for average values:<br />
<br />
<center><br />
{| class="wikitable" style="text-align:center; width:50%;"<br />
|-<br />
! scope="col" | Orientation<br />
! scope="col" | +<br />
! scope="col" | -<br />
! scope="col" | Total<br />
|-<br />
! scope="row" | Gay<br />
| 36<br />
| 64<br />
| 100<br />
|-<br />
! scope="row" | Straight<br />
| 22<br />
| 78<br />
| 100<br />
|-<br />
! scope="row" | Total<br />
| 58<br />
| 142<br />
| 200<br />
|}<br />
</center><br />
This leads to Prob (success) = (36 + 78)/200 = .57; Prob (error) = (64 +22)/200 = .43<br />
In effect, the model could be hidden tosses of a coin and the subjects, in an ESP fashion, guess heads or tails before the toss. Of course, a Bayesian would then assume a prior distribution and combine that with the results of the study to obtain a posterior probability and avoid any mention of p-value based on .5 as the null.<br />
<br />
3. Why might the following be a useful way of assessing gaydar via a modification of the procedure used in the article? Repeat the second experiment with no gays.<br />
<br />
4. Why might the following be a useful way of assessing gaydar via a modification of the procedure used in the article? Repeat the second experiment with no straights.<br />
<br />
5. In case the reader feels that Discussion #3 and #4 are deceptive, see<br />
[http://www.psychwiki.com/wiki/Deception_(methodological_technique) this Psychwiki] which looks at the history of deception in social psychology:<br />
<blockquote><br />
deception can often be seen in the “cover story” for the study, which provides the participant with a justification for the procedures and measures used. The ultimate goal of using deception in research is to ensure that the behaviors or reactions observed in a controlled laboratory setting are as close to those behaviors and reactions that occur outside of the laboratory setting. </blockquote><br />
<br />
Submitted by Paul Alper<br />
<br />
==Coin experiments==<br />
[http://online.wsj.com/article/SB10001424052702303753904577454431281272936.html?KEYWORDS=boese “The Pleasures of Suffering for Science”], <i>The Wall Street Journal</i>, June 8, 2012<br><br />
<br />
This story, about scientists experimenting on themselves, included a reference to a coin-tossing experiment:<br />
<blockquote>Even mathematics offers an example of physical self-sacrifice, through repetitive stress injury. University of Georgia professor Pope R. Hill flipped a coin 100,000 times to prove that heads and tails would come up an approximately equal number of times. The experiment lasted a year. He fell sick but completed the count, though he had to enlist the aid of an assistant near the end.</blockquote><br />
A Google search for Prof. Hill turned up the following story at the [http://www.weirduniverse.net/blog/comments/testing_the_law_of_probability “Weird Science”] website:<br />
<blockquote> If you repeatedly flip a coin, the law of probability states that approximately half the time you should get heads and half the time tails. But does this law hold true in practice?<br> <br />
Pope R. Hill, a professor at the University of Georgia during the 1930s, wanted to find out. But he thought coin-flipping was too imprecise a measurement, since any one coin might be imbalanced, causing it to favor heads or tails.<br><br />
Instead, he filled a can with 200 pennies. Half were dated 1919, half dated 1920. He shook up the can, withdrew a coin, and recorded its date. Then he returned the coin to the can. He repeated this procedure 100,000 times!<br><br />
Of the 100,000 draws, 50,145 came out 1920. 49,855 came out 1919. Hill concluded that the law of half and half does work out in practice.</blockquote><br />
<br />
===Discussion===<br />
1. Do you think that drawing a single coin from among 1919 and 1920 coins - even in a perfectly shaken can - would solve the problem of potential imbalance between heads and tails on any single coin toss? Can you think of any possible imbalance in the former case?<br><br />
2. In the second story, there is a remarkable relationship between Hill’s final counts. What questions, if any, might it raise in your mind about the experiment?<br><br />
3. Which is the more accurate expectation from a coin-tossing experiment: (a) “heads and tails would come up an approximately equal number of times” (first story) or (b) “approximately half the time you should get heads and half the time tails” (second story)?<br><br />
<br />
Submitted by Margaret Cibes<br />
<br />
'''Note''': In the [http://www.dartmouth.edu/~chance/chance_news/recent_news/chance_news_11.01.html#item8 archives of the Chance Newsletter] there is a description of some other historical attempts to empirically demonstrate the chance of heads. We read there:<br />
<blockquote>The French naturalist Count Buffon (1707-1788), know to us from the Buffon needle problem, tossed a coin 4040 times with heads coming up 2048 or 50.693 percent of the time. Karl Pearson tossed a coin 24,000 times with head coming up 12,012 or 50.05 percent of the time. While imprisoned by the Germans in the second world war, South African mathematician John Kerrich tossed a coin 10,000 times with heads coming up 5067 or 50.67 percent of the time. You can find his data in the classic text, ''Statistics'', by Freedman, Pisani and Purves.</blockquote><br />
<br />
==New presidential poll may be outlier==<br />
[http://www.huffingtonpost.com/2012/06/20/bloomberg-poll-barack-obama-lead_n_1612758.html?utm_source=Triggermail&utm_medium=email&utm_term=Daily%20Brief&utm_campaign=daily_brief “Bloomberg Poll Shows Big But Questionable Obama Lead”]<br><br />
Huffington Post, June 20, 2012 <br><br />
<br />
A Bloomberg News national poll shows Obama leading his Republican challenger by a “surprisingly large margin of 53 to 40 percent,” instead of the (at most) single-digit margin shown in other recent polls.<br> <br />
<br />
While a Bloomberg representative expressed the same surprise as others, she stated that this result is based on a sample with the same demographics as its previous polls and on its usual methodology.<br> <br />
<br />
The article’s author states:<br />
<blockquote> The most likely possibility is that this poll simply represents a statistical outlier. Yes, with a 3 percent margin of error, its Obama advantage of 53 to 40 percent is significantly different than the low single-digit lead suggested by the polling averages. However, that margin of error assumes a 95 percent level of confidence, which in simpler language means that one poll estimate in 20 will fall outside the margin of error by chance alone.</blockquote><br />
See Bloomberg’s report about the poll [http://www.bloomberg.com/news/2012-06-20/obama-leads-in-poll-as-voters-view-romney-as-out-of-touch.html here].<br><br />
<br />
Submitted by Margaret Cibes<br />
<br />
===Further discussion from FiveThirtyEight===<br />
[http://fivethirtyeight.blogs.nytimes.com/2012/06/20/outlier-polls-are-no-substitute-for-news/ Outlier polls are no substitute for news]<br><br />
by Nate Silver, FiveThirtyEight blog, ''New York Times'', 20 June 2012<br />
<br />
Silver identifies two options for dealing with such a poll, which a number of news sources have describe as an "outlier." One could simply choose to disregard it, or else "include it in some sort of average and then get on with your life." He with the following excellent advice:<br />
<blockquote><br />
My general view...is that you should not throw out data without a good reason. If cherry-picking the two or three data points that you like the most is a sin of the first order, disregarding the two or three data points that you like the least will lead to many of the same problems.<br />
</blockquote><br />
<br />
In the case of the Bloomberg poll, because the organization has a good record on accuracy, he has chosen to include it the overall average of poll results that he uses for FiveThirtyEight forecasts.<br />
<br />
Description of one of the further adjustments that Silver makes in his model can be found in his later post [http://fivethirtyeight.blogs.nytimes.com/2012/06/22/calculating-house-effects-of-polling-firms/ Calculating ‘house effects’ of polling firms] (22 June 2012). Silver explains that what often is interpreted as movement in public opinion as measured in two different polls can instead be a reflection of systematic tendencies of polling organizations to favor either Democratic or Republican candidates. Reproduced below is a chart from the post that shows the size and direction of this house effect for some major organizations:<br />
<br />
<center>[[File:Fivethirtyeight-poll-bias.png]]</center><br />
<br />
As described there, "The house effect adjustment is calculated by applying a regression analysis that compares the results of different polling firms’ surveys in the same states...The regression analysis makes these comparisons across all combinations of polling firms and states, and comes up with an overall estimate of the house effect as a result." Looking at the table, it is interesting to note that these effects are comparable to the stated margin of sampling error for typical national polls.<br />
<br />
Submitted by Bill Peterson<br />
<br />
==Rock-paper-scissors in Texas elections==<br />
[http://online.wsj.com/article/SB10001424052702303703004577476361108859928.html?KEYWORDS=nathan+koppel “Elections are a Crap Shoot in Texas, Where a Roll of the Dice Can Win”]<br><br />
by Nathan Koppel, <i>The Wall Street Journal</i>, June 19, 2012<br><br />
<br />
The state of Texas permits tied candidates to agree to “settle the matter by a game of chance.” The article describes instances of candidates using a die or a coin to decide an election.<br><br />
<br />
In one case, “leaving nothing to chance, the city attorney drafted a three-page agreement ahead of time detailing how the flip would be conducted.”<br><br />
<br />
However, not any game is permitted:<br />
<blockquote>Tonya Roberts, city secretary for Rice … consulted the Texas secretary of state's office after a city-council race ended last month in a 25-25 tie. She asked whether the race could be settled with a game of "rock, paper, scissors" but was told no. "I guess some people do consider that a game of skill," she said.</blockquote><br />
For some suggested strategies for winning this game, see [http://www.wikihow.com/Win-at-Rock,-Paper,-Scissors “How to Win at Rock, Paper, Scissors”] in wikiHow, and/or [http://blogs.discovermagazine.com/notrocketscience/2011/07/19/to-win-at-rock-paper-scissors-put-on-a-blindfold/ “To win at rock-paper-scissors, put on a blindfold”], in Discover Magazine.<br />
<br />
===Discussion===<br />
Assume that the use of the rock-paper-scissors game had <i>not</i> been suggested by one of the Rice candidates, who might have been an experienced player. Do you think that a one-time play of this game, between random strangers, could have been considered a game of chance? Why or why not?<br><br />
<br />
Submitted by Margaret Cibes<br />
<br />
==NSF may stop funding a “soft” science==<br />
[http://www.nytimes.com/2012/06/24/opinion/sunday/political-scientists-are-lousy-forecasters.html?_r=1&pagewanted=all “Political Scientists Are Lousy Forecasters”]<br><br />
by Jacqueline Stevens, <i>The New York Times</i>, June 23, 2012<br><br />
<br />
A Northwestern University political science professor has written an op-ed piece responding to a House-passed amendment that would eliminate NSF grants to political scientists. To date the Senate has not voted on the bill.<br><br />
<br />
She provides several anecdotes about political scientists having made incorrect predictions and states that she is “sympathetic with the [group] behind this amendment.” She feels that:<br />
<blockquote>[T]he government — disproportionately — supports research that is amenable to statistical analyses and models even though everyone knows the clean equations mask messy realities that contrived data sets and assumptions don’t, and can’t, capture. …. It’s an open secret in my discipline: in terms of accurate political predictions …, my colleagues have failed spectacularly and wasted colossal amounts of time and money. …. Many of today’s peer-reviewed studies offer trivial confirmations of the obvious and policy documents filled with egregious, dangerous errors. ….I look forward to seeing what happens to my discipline and politics more generally once we stop mistaking probability studies and statistical significance for knowledge.</blockquote><br />
===Discussion===<br />
1. The author makes a number of categorical statements based on anecdotal evidence. Could her conclusions about political science research be an example of the [http://en.wikipedia.org/wiki/Availability_heuristic “availability heuristic/fallacy”]?<br><br />
2. Do you think that the problems the author identifies are limited to, or at least more common in, the area of political science than in the other "soft," or even any "hard," sciences? What information would you need in order to confirm/reject your opinion?<br><br />
<br />
(Disclosure: The submitter's spouse is a political scientist, whose Ph.D. program, including stats, was entirely funded by a government act (National Defense Education Act), but who is also skeptical about <i>some</i> social science research.)<br />
<br />
Submitted by Margaret Cibes at the suggestion of James Greenwood<br />
<br />
==Crowdsourcing and its failed prediction on health care law==<br />
<br />
[http://www.nytimes.com/2012/07/08/sunday-review/when-the-crowd-isnt-wise.html When the Crowd Isn't Wise] by David Leonhardt, The New York Times, July 7, 2012.<br />
<br />
Many people were surprised at the U.S. Supreme Court ruling that upheld most aspects of the Affordable Care Act (ACA). That included a prominent source that relied on crowdsoucing. Intrade, an online prediction market, estimated a 75% chance that the individual insurance mandate would be ruled unconstitutional. This prediction held steady in spite of some late swirling rumors about the Supreme Court decision.<br />
<br />
<blockquote>With the rumors swirling, I began to check the odds at Intrade, the online prediction market where people can bet on real-world events, several times a day. The odds had barely budged. They continued to show about a 75 percent chance that the law’s so-called mandate would be ruled unconstitutional, right up until the morning it was ruled constitutional.</blockquote><br />
<br />
The concept of crowdsourcing has been [http://test.causeweb.org/wiki/chance/index.php/Chance_News_15#The_future_divined_by_the_crowd discussed on Chance News] before. The basic concept is that individual experts have systematic biases, but these biases cancel out when averaged over a large number of people.<br />
<br />
Crowdsourcing does have some notable successes, but it isn't perfect.<br />
<br />
<blockquote>For one thing, many of the betting pools on Intrade and Betfair attract relatively few traders, in part because using them legally is cumbersome. (No, I do not know from experience.) The thinness of these markets can cause them to adjust too slowly to new information.</blockquote><br />
<br />
<blockquote>And there is this: If the circle of people who possess information is small enough — as with the selection of a vice president or pope or, arguably, a decision by the Supreme Court — the crowds may not have much wisdom to impart. “There is a class of markets that I think are basically pointless,” says Justin Wolfers, an economist whose research on prediction markets, much of it with Eric Zitzewitz of Dartmouth, has made him mostly a fan of them. “There is no widely available public information.”</blockquote><br />
<br />
So, should you return to the individual expert for prediction? Maybe not.<br />
<br />
<blockquote>Mutual fund managers, as a class, lose their clients’ money because they do not outperform the market and charge fees for their mediocrity. Sports pundits have a dismal record of predicting games relative to the Las Vegas odds, which are just another market price. As imperfect as prediction markets are in forecasting elections, they have at least as good a recent record as polls. Or consider the housing bubble: both the market and most experts missed it. </blockquote><br />
<br />
Mr. Leonhardt offers a middle path.<br />
<br />
<blockquote>The answer, I think, is to take the best of what both experts and markets have to offer, realizing that the combination of the two offers a better window onto the future than either alone. Markets are at their best when they can synthesize large amounts of disparate information, as on an election night. Experts are most useful when a system exists to identify the most truly knowledgeable — a system that often resembles a market.</blockquote><br />
<br />
This last sentence introduces the thought that you use crowdsourcing to find the best experts. Social media like Twitter allows people an interesting way to identify experts who are truly experts.<br />
<br />
<blockquote>Think for a moment about what a Twitter feed is: it’s a personalized market of experts (and friends), in which you can build your own focus group and listen to its collective analysis about the past, present and future. An RSS feed, in which you choose blogs to read, works similarly. You make decisions about which experts are worthy of your attention, based both on your own judgments about them and on other experts’ judgments.</blockquote><br />
<br />
<blockquote>Their predictions now face a market discipline that did not always exist before the Internet came along. “Experts exist,” as Mr. Wolfers says, “but they’re not necessarily the same as the guys on TV.” </blockquote><br />
<br />
===Questions===<br />
<br />
1. How bad did Intrade really perform on the Supreme Court decision on ACA? How large a probability does a system like Intrade have to place on a bad prediction to cause you to lose faith in it?<br />
<br />
2. What are some of the potential problems with identifying experts by the number of retweets that they get in Twitter?</div>Simon66217https://www.causeweb.org/wiki/chance/index.php?title=Chance_News_86&diff=16133Chance News 862012-07-09T18:22:59Z<p>Simon66217: /* NSF may stop funding a “soft” science */</p>
<hr />
<div>==Quotations==<br />
"Asymptotically we are all dead."<br />
<div align=right>--paraphrase of Keynes, sometimes attributed to Melvin R. Novick</div><br />
<br />
Submitted by Paul Alper<br />
<br />
----<br />
"To err is human, to forgive divine but to include errors in your design is statistical."<br />
<div align=right>--Leslie Kish, in [http://asapresidentialpapers.info/documents/Kish_Leslie_1977_edit_(wla_092809).pdf Chance, statistics, and statisticians], 1977 ASA Presidential Address</div><br />
<br />
Submitted by Bill Peterson<br />
<br />
----<br />
"The only statistical test one ever needs is the IOTT or 'interocular trauma test.' The result just hits one between the eyes. If one needs any more statistical analysis, one should be working harder to control sources of error, or perhaps studying something else entirely."<br />
<br />
<div align=right>--David H. Krantz, in [http://www.unt.edu/rss/class/mike/5030/articles/krantznhst.pdf The null hypothesis testing in psychology], ''JASA'', December 1999, p. 1373.</div><br />
<br />
Krantz is describing how some psychologists view statistical testing. On the same page he describes another viewpoint:<br />
<br />
:"Nothing is due to chance. This is the Freudian stance...but fortunately, it has little support among researchers."<br />
<br />
Submitted by Paul Alper<br />
<br />
==Forsooth==<br />
“The survey interviewed 991 Americans online from June 28-30. The precision of the Reuters/Ipsos online polls is measured using a <i>credibility</i> interval. In this case, the poll has a <i>credibility</i> interval of plus or minus 3.6 percentage points.” (emphasis added)<br />
<div align=right>[http://www.huffingtonpost.com/2012/07/01/obamacare-supreme-court-ruling_n_1641560.html?ref=topbar “Obamacare Support Rises After Supreme Court Ruling, Poll Finds”]<br><br />
<i>Huffington Post</i>, July 1, 2012</div><br />
<br />
Submitted by Margaret Cibes<br />
<br />
==Gaydar==<br />
[http://www.nytimes.com/2012/06/03/opinion/sunday/the-science-of-gaydar.html The science of ‘gaydar’]<br><br />
by Joshua A. Tabak and Vivian Zayas, ''New York Times'', 3 June 2012<br />
<br />
The definition of GAYDAR is the "Ability to sense a homosexual" according to [http://www.internetslang.com/GAYDAR-meaning-definition.asp internetslang.com.] <br />
<br />
In their NYT article, Tabak and Zayas write<br />
<blockquote><br />
Should you trust your gaydar in everyday life? Probably not. In our experiments, average gaydar judgment accuracy was only in the 60 percent range. This demonstrates gaydar ability — which is far from judgment proficiency. But is gaydar real? Absolutely. <br />
</blockquote><br />
At [http://www.plosone.org/article/info%3Adoi%2F10.1371%2Fjournal.pone.0036671 PLoS One] is their complete research paper where subjects viewed facial photographs of homosexuals and straights; the subjects then had a short time to decide the sexual orientation. In their first experiment, the faces were shown upright only:<br />
<blockquote><br />
Twenty-four University of Washington students (19 women; age range = 18–22 years) participated in exchange for extra course credit. Data from seven additional participants were excluded from analyses due to failure to follow instructions (n = 4) or computer malfunction (n = 3). <br />
</blockquote><br />
<br />
In the second experiment, faces were shown upright and upside-down and the subjects had a short time to decide the sexual orientation:<br />
<blockquote><br />
One hundred twenty-nine University of Washington students (92 women; age range = 18–25 years) participated in exchange for extra course credit. Data from 16 additional participants were excluded from analyses due to failure to follow instructions (n = 12) or average reaction times more than 3 SD above the mean (n = 4). </blockquote><br />
<br />
According to the authors, “there are two components of “accuracy”: the hit rate which is “the proportion of gay faces correctly perceived as gay, and the false alarm rate” which is “the proportion of straight faces incorrectly perceived as gay.” The figure reproduced below (full version [http://www.plosone.org/article/slideshow.action?uri=info:doi/10.1371/journal.pone.0036671&imageURI=info:doi/10.1371/journal.pone.0036671.g003 here]) indicates that the accuracy was better when the target gender was women as opposed to men and the accuracy was better when the faces were upright as opposed to upside down. Presumably, random guessing would produce an “accuracy” of about .5.<br />
<br />
::[[File:TabakZayas_fig3.png ]]<br />
<br />
::Figure 3. Accuracy of detecting sexual orientation from upright and upside-down faces (Experiment 2).<br />
::Mean accuracy (A′) in judging sexual orientation from faces presented for 50 milliseconds as a function of the target’s gender and spatial orientation (upright or upside-down; Experiment 2). Judgments of upright faces are based on both configural and featural processing, whereas judgments of upside-down faces are based only on featural face processing. Error bars represent ±1 SEM.<br />
<br />
Reproduced below are the detailed results for the second experiment:<br />
::[[File:TabakZayas_table1.png]]<br />
<br />
::Table 1. Hit and False Alarm Rates for Snap Judgments of Sexual Orientation in Experiment 2.<br />
<br />
The author’s conclude with the following statement:<br />
<blockquote><br />
<br />
The present research is the first to demonstrate (a) that configural face processing significantly contributes to perception of sexual orientation, and (b) that sexual orientation is inferred more easily from women’s vs. men’s faces. In light of these findings, it is interesting to note the popular desire to learn to read faces like books. Considering how challenging it is to read a book upside-down, it seems that we read faces better than we read books.<br />
</blockquote><br />
<br />
===Discussion===<br />
<br />
1. Gaydar "accuracy" seems to be defined in the paper as hit rate / (hit rate + false alarm rate) or, to use terms common in [http://en.wikipedia.org/wiki/Sensitivity_and_specificity medical tests,] positive predictive value = true positive / (true positive + false positive). The paper makes no mention of negative predictive value = true negative / (true negative + false negative). As is illustrated in the Wikipedia article, legitimate medical tests will tolerate a low positive predictive value because a more expensive test exists in the rare case that the disease is actually present; negative predictive values must be high to avoid potentially deadly false optimism. The situation here is somewhat different because the subjects were exposed to an approximately equal number of gays and straights whereas in medical tests, most people in the population do not have the “disease.”<br />
<br />
2. Perhaps the analogy with medical testing is inappropriate. That is, an error is an error and no distinction should be made between the two types of errors. Consider the above table for the case of women and upright spatial orientation. The hit rate is .36 and the false alarm rate is .22. If we assume that the 67 subjects viewed 100 gay faces and 100 straight faces, then we obtain the following table for average values:<br />
<br />
<center><br />
{| class="wikitable" style="text-align:center; width:50%;"<br />
|-<br />
! scope="col" | Orientation<br />
! scope="col" | +<br />
! scope="col" | -<br />
! scope="col" | Total<br />
|-<br />
! scope="row" | Gay<br />
| 36<br />
| 64<br />
| 100<br />
|-<br />
! scope="row" | Straight<br />
| 22<br />
| 78<br />
| 100<br />
|-<br />
! scope="row" | Total<br />
| 58<br />
| 142<br />
| 200<br />
|}<br />
</center><br />
This leads to Prob (success) = (36 + 78)/200 = .57; Prob (error) = (64 +22)/200 = .43<br />
In effect, the model could be hidden tosses of a coin and the subjects, in an ESP fashion, guess heads or tails before the toss. Of course, a Bayesian would then assume a prior distribution and combine that with the results of the study to obtain a posterior probability and avoid any mention of p-value based on .5 as the null.<br />
<br />
3. Why might the following be a useful way of assessing gaydar via a modification of the procedure used in the article? Repeat the second experiment with no gays.<br />
<br />
4. Why might the following be a useful way of assessing gaydar via a modification of the procedure used in the article? Repeat the second experiment with no straights.<br />
<br />
5. In case the reader feels that Discussion #3 and #4 are deceptive, see<br />
[http://www.psychwiki.com/wiki/Deception_(methodological_technique) this Psychwiki] which looks at the history of deception in social psychology:<br />
<blockquote><br />
deception can often be seen in the “cover story” for the study, which provides the participant with a justification for the procedures and measures used. The ultimate goal of using deception in research is to ensure that the behaviors or reactions observed in a controlled laboratory setting are as close to those behaviors and reactions that occur outside of the laboratory setting. </blockquote><br />
<br />
Submitted by Paul Alper<br />
<br />
==Coin experiments==<br />
[http://online.wsj.com/article/SB10001424052702303753904577454431281272936.html?KEYWORDS=boese “The Pleasures of Suffering for Science”], <i>The Wall Street Journal</i>, June 8, 2012<br><br />
<br />
This story, about scientists experimenting on themselves, included a reference to a coin-tossing experiment:<br />
<blockquote>Even mathematics offers an example of physical self-sacrifice, through repetitive stress injury. University of Georgia professor Pope R. Hill flipped a coin 100,000 times to prove that heads and tails would come up an approximately equal number of times. The experiment lasted a year. He fell sick but completed the count, though he had to enlist the aid of an assistant near the end.</blockquote><br />
A Google search for Prof. Hill turned up the following story at the [http://www.weirduniverse.net/blog/comments/testing_the_law_of_probability “Weird Science”] website:<br />
<blockquote> If you repeatedly flip a coin, the law of probability states that approximately half the time you should get heads and half the time tails. But does this law hold true in practice?<br> <br />
Pope R. Hill, a professor at the University of Georgia during the 1930s, wanted to find out. But he thought coin-flipping was too imprecise a measurement, since any one coin might be imbalanced, causing it to favor heads or tails.<br><br />
Instead, he filled a can with 200 pennies. Half were dated 1919, half dated 1920. He shook up the can, withdrew a coin, and recorded its date. Then he returned the coin to the can. He repeated this procedure 100,000 times!<br><br />
Of the 100,000 draws, 50,145 came out 1920. 49,855 came out 1919. Hill concluded that the law of half and half does work out in practice.</blockquote><br />
<br />
===Discussion===<br />
1. Do you think that drawing a single coin from among 1919 and 1920 coins - even in a perfectly shaken can - would solve the problem of potential imbalance between heads and tails on any single coin toss? Can you think of any possible imbalance in the former case?<br><br />
2. In the second story, there is a remarkable relationship between Hill’s final counts. What questions, if any, might it raise in your mind about the experiment?<br><br />
3. Which is the more accurate expectation from a coin-tossing experiment: (a) “heads and tails would come up an approximately equal number of times” (first story) or (b) “approximately half the time you should get heads and half the time tails” (second story)?<br><br />
<br />
Submitted by Margaret Cibes<br />
<br />
'''Note''': In the [http://www.dartmouth.edu/~chance/chance_news/recent_news/chance_news_11.01.html#item8 archives of the Chance Newsletter] there is a description of some other historical attempts to empirically demonstrate the chance of heads. We read there:<br />
<blockquote>The French naturalist Count Buffon (1707-1788), know to us from the Buffon needle problem, tossed a coin 4040 times with heads coming up 2048 or 50.693 percent of the time. Karl Pearson tossed a coin 24,000 times with head coming up 12,012 or 50.05 percent of the time. While imprisoned by the Germans in the second world war, South African mathematician John Kerrich tossed a coin 10,000 times with heads coming up 5067 or 50.67 percent of the time. You can find his data in the classic text, ''Statistics'', by Freedman, Pisani and Purves.</blockquote><br />
<br />
==New presidential poll may be outlier==<br />
[http://www.huffingtonpost.com/2012/06/20/bloomberg-poll-barack-obama-lead_n_1612758.html?utm_source=Triggermail&utm_medium=email&utm_term=Daily%20Brief&utm_campaign=daily_brief “Bloomberg Poll Shows Big But Questionable Obama Lead”]<br><br />
Huffington Post, June 20, 2012 <br><br />
<br />
A Bloomberg News national poll shows Obama leading his Republican challenger by a “surprisingly large margin of 53 to 40 percent,” instead of the (at most) single-digit margin shown in other recent polls.<br> <br />
<br />
While a Bloomberg representative expressed the same surprise as others, she stated that this result is based on a sample with the same demographics as its previous polls and on its usual methodology.<br> <br />
<br />
The article’s author states:<br />
<blockquote> The most likely possibility is that this poll simply represents a statistical outlier. Yes, with a 3 percent margin of error, its Obama advantage of 53 to 40 percent is significantly different than the low single-digit lead suggested by the polling averages. However, that margin of error assumes a 95 percent level of confidence, which in simpler language means that one poll estimate in 20 will fall outside the margin of error by chance alone.</blockquote><br />
See Bloomberg’s report about the poll [http://www.bloomberg.com/news/2012-06-20/obama-leads-in-poll-as-voters-view-romney-as-out-of-touch.html here].<br><br />
<br />
Submitted by Margaret Cibes<br />
<br />
===Further discussion from FiveThirtyEight===<br />
[http://fivethirtyeight.blogs.nytimes.com/2012/06/20/outlier-polls-are-no-substitute-for-news/ Outlier polls are no substitute for news]<br><br />
by Nate Silver, FiveThirtyEight blog, ''New York Times'', 20 June 2012<br />
<br />
Silver identifies two options for dealing with such a poll, which a number of news sources have describe as an "outlier." One could simply choose to disregard it, or else "include it in some sort of average and then get on with your life." He with the following excellent advice:<br />
<blockquote><br />
My general view...is that you should not throw out data without a good reason. If cherry-picking the two or three data points that you like the most is a sin of the first order, disregarding the two or three data points that you like the least will lead to many of the same problems.<br />
</blockquote><br />
<br />
In the case of the Bloomberg poll, because the organization has a good record on accuracy, he has chosen to include it the overall average of poll results that he uses for FiveThirtyEight forecasts.<br />
<br />
Description of one of the further adjustments that Silver makes in his model can be found in his later post [http://fivethirtyeight.blogs.nytimes.com/2012/06/22/calculating-house-effects-of-polling-firms/ Calculating ‘house effects’ of polling firms] (22 June 2012). Silver explains that what often is interpreted as movement in public opinion as measured in two different polls can instead be a reflection of systematic tendencies of polling organizations to favor either Democratic or Republican candidates. Reproduced below is a chart from the post that shows the size and direction of this house effect for some major organizations:<br />
<br />
<center>[[File:Fivethirtyeight-poll-bias.png]]</center><br />
<br />
As described there, "The house effect adjustment is calculated by applying a regression analysis that compares the results of different polling firms’ surveys in the same states...The regression analysis makes these comparisons across all combinations of polling firms and states, and comes up with an overall estimate of the house effect as a result." Looking at the table, it is interesting to note that these effects are comparable to the stated margin of sampling error for typical national polls.<br />
<br />
Submitted by Bill Peterson<br />
<br />
==Rock-paper-scissors in Texas elections==<br />
[http://online.wsj.com/article/SB10001424052702303703004577476361108859928.html?KEYWORDS=nathan+koppel “Elections are a Crap Shoot in Texas, Where a Roll of the Dice Can Win”]<br><br />
by Nathan Koppel, <i>The Wall Street Journal</i>, June 19, 2012<br><br />
<br />
The state of Texas permits tied candidates to agree to “settle the matter by a game of chance.” The article describes instances of candidates using a die or a coin to decide an election.<br><br />
<br />
In one case, “leaving nothing to chance, the city attorney drafted a three-page agreement ahead of time detailing how the flip would be conducted.”<br><br />
<br />
However, not any game is permitted:<br />
<blockquote>Tonya Roberts, city secretary for Rice … consulted the Texas secretary of state's office after a city-council race ended last month in a 25-25 tie. She asked whether the race could be settled with a game of "rock, paper, scissors" but was told no. "I guess some people do consider that a game of skill," she said.</blockquote><br />
For some suggested strategies for winning this game, see [http://www.wikihow.com/Win-at-Rock,-Paper,-Scissors “How to Win at Rock, Paper, Scissors”] in wikiHow, and/or [http://blogs.discovermagazine.com/notrocketscience/2011/07/19/to-win-at-rock-paper-scissors-put-on-a-blindfold/ “To win at rock-paper-scissors, put on a blindfold”], in Discover Magazine.<br />
<br />
===Discussion===<br />
Assume that the use of the rock-paper-scissors game had <i>not</i> been suggested by one of the Rice candidates, who might have been an experienced player. Do you think that a one-time play of this game, between random strangers, could have been considered a game of chance? Why or why not?<br><br />
<br />
Submitted by Margaret Cibes<br />
<br />
==NSF may stop funding a “soft” science==<br />
[http://www.nytimes.com/2012/06/24/opinion/sunday/political-scientists-are-lousy-forecasters.html?_r=1&pagewanted=all “Political Scientists Are Lousy Forecasters”]<br><br />
by Jacqueline Stevens, <i>The New York Times</i>, June 23, 2012<br><br />
<br />
A Northwestern University political science professor has written an op-ed piece responding to a House-passed amendment that would eliminate NSF grants to political scientists. To date the Senate has not voted on the bill.<br><br />
<br />
She provides several anecdotes about political scientists having made incorrect predictions and states that she is “sympathetic with the [group] behind this amendment.” She feels that:<br />
<blockquote>[T]he government — disproportionately — supports research that is amenable to statistical analyses and models even though everyone knows the clean equations mask messy realities that contrived data sets and assumptions don’t, and can’t, capture. …. It’s an open secret in my discipline: in terms of accurate political predictions …, my colleagues have failed spectacularly and wasted colossal amounts of time and money. …. Many of today’s peer-reviewed studies offer trivial confirmations of the obvious and policy documents filled with egregious, dangerous errors. ….I look forward to seeing what happens to my discipline and politics more generally once we stop mistaking probability studies and statistical significance for knowledge.</blockquote><br />
===Discussion===<br />
1. The author makes a number of categorical statements based on anecdotal evidence. Could her conclusions about political science research be an example of the [http://en.wikipedia.org/wiki/Availability_heuristic “availability heuristic/fallacy”]?<br><br />
2. Do you think that the problems the author identifies are limited to, or at least more common in, the area of political science than in the other "soft," or even any "hard," sciences? What information would you need in order to confirm/reject your opinion?<br><br />
<br />
(Disclosure: The submitter's spouse is a political scientist, whose Ph.D. program, including stats, was entirely funded by a government act (National Defense Education Act), but who is also skeptical about <i>some</i> social science research.)<br />
<br />
Submitted by Margaret Cibes at the suggestion of James Greenwood<br />
<br />
==Crowdsourcing and its failed prediction on health care law==<br />
<br />
[http://www.nytimes.com/2012/07/08/sunday-review/when-the-crowd-isnt-wise.html When the Crowd Isn't Wise] by David Leonhardt, The New York Times, July 7, 2012.<br />
<br />
Many people were surprised at the U.S. Supreme Court ruling that upheld most aspects of the Affordable Care Act (ACA). That included a prominent source that relied on crowdsoucing. Intrade, an online prediction market, estimated a 75% chance that the individual insurance mandate would be ruled unconstitutional. This prediction held steady in spite of some late swirling rumors about the Supreme Court decision.<br />
<br />
<blockquote>With the rumors swirling, I began to check the odds at Intrade, the online prediction market where people can bet on real-world events, several times a day. The odds had barely budged. They continued to show about a 75 percent chance that the law’s so-called mandate would be ruled unconstitutional, right up until the morning it was ruled constitutional.</blockquote><br />
<br />
The concept of crowdsourcing has been [http://test.causeweb.org/wiki/chance/index.php/Chance_News_15#The_future_divined_by_the_crowd discussed on Chance News] before.</div>Simon66217https://www.causeweb.org/wiki/chance/index.php?title=Chance_News_85&diff=15947Chance News 852012-05-21T18:45:34Z<p>Simon66217: /* Forsooth */</p>
<hr />
<div>==Quotations==<br />
<br />
“Journalists could help people grasp uncertainty and help them apply critical thinking to health care decision-making issues…rather than promote false certainty, shibboleths and non-evidence-based, cheerleading advocacy.”<br />
<br />
<div align=right>-- Gary Schwitzer, at [http://www.healthnewsreview.org/2012/04/grasping-and-even-celebrating-uncertainty/ HealthNewsReview.org]</div><br />
<br />
"To treat your facts with imagination is one thing; to imagine your facts is another."<br />
<div align=right>-- John Burroughs (1837-1921), quoted in Gary's [http://www.slideshare.net/HealthNewsReview/uw-talk-for-slideshare talk] at the conference <br>[http://sciencedenial.wisc.edu/ Science Writing in the Age of Denial], University of Wisconsin, Madison </div><br />
<br />
Submitted by Paul Alper<br />
<br />
----<br />
"A big computer, a complex algorithm and a long time does not equal science." <br />
<br />
<div align=right>-- Robert Gentleman, quoted at [http://onertipaday.blogspot.com/ One R Tip A Day]</div><br />
Submitted by Bill Peterson<br />
----<br />
"Statistics [from observational studies] cannot turn sow's ears into silk purses, no matter how large the number of sow's ears available for study. Nor can adding up large numbers of scientifically impoverished studies yield scientific information. The appeal of statistics is that it is (a) very cheap compared to scientific testing, and (b) it can produce results to order because the data itself imposes relatively few constraints on the statistical conclusion drawn from it. Both of these render such methods irresistible to politicians and advocacy groups.”<br />
<div align=right>Stephen Krumpe blogging[http://online.wsj.com/article/SB10001424052702303916904577377841427001840.html?KEYWORDS=gautam+naik&_nocache=1336253792402&user=welcome&mg=id-wsj#articleTabs%3Dcomments] in response to<br><br />
[http://online.wsj.com/article/SB10001424052702303916904577377841427001840.html?KEYWORDS=gautam+naik&_nocache=1336253792402&user=welcome&mg=id-wsj “Analytical Trend Troubles Scientists”], <i>The Wall Street Journal</i>, May 4, 2012</div><br />
Submitted by Margaret Cibes<br />
<br />
----<br />
“The first principle [of scientific integrity] is that you must not fool yourself – and you are the easiest person to fool. …. I would like to add something that’s not essential to the science, but something I kind of believe, which is that you should not fool the layman when you’re talking as a scientist. ….One example of the principle is this: If you’ve made up your mind to test a theory, or you want to explain some idea, you should always decide to publish it whichever way it comes out. If we only publish results of a certain kind, we can make the argument look good. We must publish <i>both</i> kinds of results.”<br />
<br />
<div align=right>Richard Feynman, in [http://calteches.library.caltech.edu/3043/1/CargoCult.pdf “Cargo Cult Science”]<br><br />
Caltech’s 1974 commencement address</div><br />
Submitted by Margaret Cibes<br />
<br />
----<br />
From <i>Significance</i> magazine, April 2012:<br><br />
<br />
“It was a cause of great sorrow to me that I had absolutely no talent for the game [Liverpool football], or any sport other than chess, and I had to accept that I am a centre forward trapped inside a statistician’s body.”<br />
<div align=right>"Dr Fisher’s casebook: Sporting life"</div><br />
“We see an apparently unending upward spiral in remarkable levels of athletic achievement …. I think a major contributor to this remarkable increase in proficiency is population size. …. A simple statistical model that captures this idea posits that human running ability has not changed over the past century. That, in both 1900 and 2000 the distribution of running ability of the human race is well characterized by a normal curve with the same average and the same variability. What has changed is how many people live under that curve. …. [T]wo factors are working together. There is the growth of the total population of the world. There is also the (non-parallel) growth of the population who can participate. …. The best of a billion is likely better than the best of a million.”<br />
<div align=right>Howard Wainer in “Piano virtuosos and the four-minute mile”</div><br />
Submitted by Margaret Cibes<br />
<br />
==Forsooth==<br />
<br />
"We're spending $70 per person to fill this out. That’s just not cost effective," he continued, "especially since in the end this is not a scientific survey. It’s a random survey." Daniel Webster, first term Republican congressman from Florida, referring to the American Community Survey, as quoted in [http://www.nytimes.com/2012/05/20/sunday-review/the-debate-over-the-american-community-survey.html "The Beginning of the End of the Census?"] Catherin Rampell, The New York Times, May 19, 2012.<br />
<br />
Submitted by Steve Simon<br />
<br />
==Fish oil==<br />
[http://well.blogs.nytimes.com/2012/04/11/weighing-the-evidence-on-fish-oils-for-heart-health/?ref=anahadoconnor&gwh=C0B11AE5752AA76BF96A15FA37421288 Weighing the evidence on fish oils for heart health]<br><br />
by Anahad O’Connor, Well blog, ''New York Times'', 11 April 2012<br />
<br />
According to O'Connor,<br />
<br />
<blockquote><br />
Fish oil supplements have become some of the most popular dietary pills on the market, largely on the strength of medical research linking diets high in baked and broiled fish to lower rates of heart disease. Across the United States, annual sales of purified fish oil, commonly sold as omega-3 fatty acids, are in the neighborhood of a billion dollars. And in some parts of Europe, doctors routinely prescribe fish oils to patients with heart disease.<br />
<br><br><br />
People who put their faith in fish oil supplements may want to reconsider. A new analysis of the evidence casts doubt on the widely touted notion that the pills can prevent heart attacks in people at risk for cardiovascular <br />
disease.<br />
</blockquote><br />
<br />
And well the people might. O’Connor is referring to [http://archinte.ama-assn.org/cgi/content/abstract/archinternmed.2012.262 “Efficacy of Omega-3 Fatty Acid Supplements (Eicosapentaenoic Acid and Docosahexaenoic Acid) in the Secondary Prevention of Cardiovascular Disease; A Meta-analysis of Randomized, Double-blind, Placebo-Controlled Trials”] by S.M. Kwak, et al., to appear in the ''Archives of Internal Medicine''. Not only did:<br />
<blockquote><br />
Our meta-analysis showed insufficient evidence of a secondary preventive effect of omega-3 fatty acid supplements against overall cardiovascular events among patients with a history of cardiovascular disease,<br />
</blockquote><br />
<br />
But also:<br />
<br />
<blockquote><br />
Furthermore, no significant preventive effect was observed in subgroup analyses by the following: country location, inland or coastal geographic area, history of CVD, concomitant medication use, type of placebo material in the trial, methodological quality of the trial, duration of treatment, dosage of eicosapentaenoic acid [EPA] or docosahexaenoic acid [DHA], or use of fish oil supplementation only as treatment.<br />
</blockquote><br />
<br />
'''Discussion'''<br />
<br />
1. The authors started their meta-analysis with 1007 articles; eventually, after 181 studies were excluded as duplicates and others were dropped out for various other reasons, they were left with “14 randomized, double blind, placebo-controlled trials.” The total number of subjects in the 14 trials was 20, 485. As stated above, statistical significance was not to be seen. Two large studies of 11,234 and 18, 645 subjects, respectively which did show beneficial effects from fish oil were not included in the 14; they were rejected because they were “open-label” studies. Why are open-label studies suspect?<br />
<br />
2. Why did the subjects in the placebo arm of the 14 studies receive various vegetable oils? Some of those subjects in the placebo arm received olive oil. Why might this “have disguised the ‘true’ benefit of omega-3 fatty acid supplementation?”<br />
<br />
3. If not fish oil, O’Connor says the authors conclude that<br />
<br />
<blockquote><br />
it may make the most sense to spend your money on actual fish, rather than fish oil supplements.<br />
<br ><br><br />
They argue that by eating fish, you end up replacing other less healthy protein sources, like processed foods and red meat. For that reason, a diet high in fatty fish — one that includes at least two servings a week — may make a difference over the long term, they say.<br />
</blockquote><br />
<br />
If the above is correct, why are so many people eschewing fish for fish oil?<br />
<br />
Submitted by Paul Alper<br />
<br />
==Choosing a spouse--really?==<br />
[http://www.nytimes.com/2012/05/06/magazine/romneys-former-bain-partner-makes-a-case-for-inequality.html?emc=eta1 The purpose of spectacular wealth, according to a spectacularly wealthy guy]<br><br />
by Adam Davidson, ''New York Times Magazine'', 1 May 2012<br />
<br />
Davidson describes an interview with Edward Conard, one of Mitt Romney's former associates from Bain Capital, who has written a book entitled ''Unintended Consequences: Why Everything You’ve Been Told About the Economy Is Wrong''. It is amusing to note the following, which appears that about halfway through the article:<br />
<blockquote><br />
There’s also the fact that Conard applies a relentless, mathematical logic to nearly everything, even finding a good spouse. He advocates, in utter seriousness, using demographic data to calculate the number of potential mates in your geographic area. Then, he says, you should set aside a bit of time for “calibration” — dating as many people as you can so that you have a sense of what the marriage marketplace is like. Then you enter the selection phase, this time with the goal of picking a permanent mate. The first woman you date who is a better match than the best woman you met during the calibration phase is, therefore, the person you should marry. By statistical probability, she is as good a match as you’re going to get. (Conard used this system himself.)<br />
</blockquote><br />
<br />
'''Discussion'''<br />
<br />
1. They almost got the description of the optimal strategy for the famous [http://en.wikipedia.org/wiki/Secretary_problem Secretary Problem] correct. What is missing from this argument?<br />
<br />
2. Do you believe that Conard actually used this system himself?<br />
<br />
Submitted by Charles Grinstead<br />
<br />
===Comments===<br />
1. This is a long article, which presents Conard's argument as a defense of unbridled, winner-take-all competition. He rejects the idea that income inequality is a problem; indeed, he thinks even greater rewards are needed as incentives for risk-taking entrepreneurs, whose efforts to boost the economy will benefit everyone. As a reminder that we have heard this before, Paul Alper sent the following quote from a previous century:<br />
<br />
<blockquote>The American Beauty Rose can be produced in the splendor and fragrance which bring cheer to its beholder only by sacrificing the early buds which grow up around it. This is not an evil tendency in business. It is merely the working-out of a law of nature and a law of God.</blockquote><br />
<div align=right>--John D. Rockefeller, address to the students of Brown University, quoted in Ida Tarbell (1904) ''The History of the Standard Oil Company''</div><br />
<br />
2. See also the post [http://economix.blogs.nytimes.com/2012/05/07/incentive-perversity/ Incentive perversity], Economix blog, ''New York Times'', 7 May 2012. University of Massachusetts economist Nancy Folbre reminds us that the links between rewards and performance are not so clear cut, and that incentives can be distorted when economic rewards grow too extreme. She points out that high-stakes educational testing led schools to cheat on exams, and out-sized contracts for star athletes led to an era tainted by performance-enhancing drugs. She concludes that, "Good incentives are always a good idea. But it’s not as easy to design them as it might seem, because they should discourage a host of economic sins — not just sloth and fear, but also cruelty and greed."<br />
<br />
==TV and the shortening of life==<br />
[http://bjsm.bmj.com/content/early/2011/08/01/bjsm.2011.085662.short?q=w_bjsm_ahead_tab Television viewing time and reduced life expectancy: a life table analysis]<br><br />
by J Lennert Veerman, et. al., ''British Journal of Sports Medicine'', 15 August 2011<br />
<br />
From the online abstract we read:<br />
<blockquote><br />
'''Results''' The amount of TV viewed in Australia in 2008 reduced life expectancy at birth by 1.8 years (95% uncertainty interval (UI): 8.4 days to 3.7 years) for men and 1.5 years (95% UI: 6.8 days to 3.1 years) for women. Compared with persons who watch no TV, those who spend a lifetime average of 6 h/day watching TV can expect to live 4.8 years (95% UI: 11 days to 10.4 years) less. On average, every single hour of TV viewed after the age of 25 reduces the viewer's life expectancy by 21.8 (95% UI: 0.3–44.7) min. This study is limited by the low precision with which the relationship between TV viewing time and mortality is currently known.<br />
<br><br><br />
'''Conclusions''' TV viewing time may be associated with a loss of life that is comparable to other major chronic disease risk factors such as physical inactivity and obesity.<br />
</blockquote><br />
<br />
Needless to say, this highly speculative--but very quotable--statistical analysis has been picked up by every conceivable web site since last August. It just made its appearance in the Minneapolis ''Star Tribune'': [http://www.startribune.com/lifestyle/150359955.html Can TV cut your life short?], by Jeff Strickler, 7 May 2012. The ''New York Times'' mentioned it a week earlier: [http://www.nytimes.com/2012/04/29/sunday-review/stand-up-for-fitness.html?_r=1&hp Don’t just sit there], by Gretchen Reynolds, 28 April 2012.<br />
<br />
Submitted by Paul Alper<br />
<br />
===Discussion===<br />
<br />
1. The NYT article describes a number of studies concerning the ill effects of inactivity. Their entire description of the Australian study reads, "researchers determined that watching an hour of television can snip 22 minutes from someone’s life. If an average man watched no TV in his adult life, the authors concluded, his life span might be 1.8 years longer, and a TV-less woman might live for a year and half longer than otherwise." What is missing here?<br />
<br />
2. Elsewhere in the story, however, the NYT notes that "Television viewing is a widely used measure of sedentary time." What does this suggest about interpreting the Australian study?<br />
<br />
=="Approval" statistic==<br />
[http://jonathanpelto.com/2012/05/07/memo-to-connecticut-democrats-only-all-others-should-skip-this-post/ “Memo to Connecticut Democrats”], by Jonathan Pelto, May 7, 2012<br><br />
<br />
From a CT blogger's website:<br />
<blockquote>The following chart indicates how Connecticut Democratic voters rate Governor Malloy’s job performance. In politics we use a statistic that measures the rate of approval compared to the rate of disapproval – we call that the overall positive or negative rating of an individual (i.e. +/-). The higher the positive rating the better the candidate or elected official is doing.</blockquote><br />
[[File:Malloy_polls.jpg]]<br />
===Questions===<br />
1. Can you think of a reason why the June 2011 poll figures sum to 106?<br><br />
2. The rightmost column heading might suggest that these figures are margins of error (percentage points), except for their size. If they had been margins of error, about how many people would have been in the sample on March 2011? Is that realistic?<br><br />
3. According to the text, the rightmost column contains a “statistic that measures the rate of approval compared to the rate of disapproval.” How do you think that the blogger compared approval/disapproval figures to come up with the figures in the rightmost column? (While I couldn’t find a definition of “approval rating,” I did found that the blogger's "statistic" is pretty common; for example, see Wikipedia's [http://en.wikipedia.org/wiki/United_States_presidential_approval_rating "United States presidential approval rating"].)<br><br />
4. How might you have entitled the +/- column, in order to clarify its meaning?<br><br />
5. The blogger opens the article by stating that Malloy's "support from members of [his] own party ... is at a breathtakingly low + 19 percent." Do you agree? What would you have said?<br><br />
<br />
Submitted by Margaret Cibes<br />
<br />
==Happiness and variability==<br />
[http://www.usatoday.com/news/health/wellness/story/2012-05-03/parents-happiness-population-survey/54767508/1? Parents today are happier than non-parents, studies suggest]<br><br />
by Sharon Jayson, ''USA Today'', 5 March 2012<br />
<br />
This article drew commentary on Andrew Gelman's blog ([http://andrewgelman.com/2012/05/happy-news-on-happiness-what-can-we-believe/ Happy news on happiness; what can we believe?], 7 May 2012). Gelman notes that the article concludes with the following quote: <blockquote>The first child increases happiness quite a lot. The second child a little. The third not at all.</blockquote> <br />
<br />
The quote is attributed to Mikko Myrskylä, the coauthor of [http://onlinelibrary.wiley.com/doi/10.1111/j.1728-4457.2011.00389.x/abstract A Global Perspective on Happiness and Fertility], one of the two studies described in the article.<br />
<br />
Gelman then complains, "As a statistician, I hate hate hate hate hate when people ignore variability and present results deterministically. The above statement might be an accurate summary of average patterns but is certainly not true in every case!"<br />
<br />
Submitted by Paul Alper<br />
<br />
==House cuts American Community Survey ==<br />
[http://www.nytimes.com/2012/05/14/opinion/operating-in-the-dark.html?_r=2 “Operating in the dark”], Editorial, <i>New York Times</i>, May 13, 2012<br><br />
[http://www.washingtonpost.com/blogs/ezra-klein/post/does-government-knowledge-mean-government-intrusion/2012/05/13/gIQAznUtMU_blog.html “Does government knowledge mean government intrusion?”], by Suzy Khimm,<i>Washington Post</i>, May 13, 2012<br><br />
<br />
Last week the U.S. House of Representatives voted to cut funds for the American Community Survey and at least part of the Economic Census. The former, “a bipartisan creation” in 2005, provides <i>annual</i> updates of economic, demographic and housing characteristics, which supplement the <i>decennial</i> census information. Let’s see what the Senate does ….<br />
<br />
See the U.S. Census Bureau Director’s statement (and video)[http://directorsblog.blogs.census.gov/2012/05/11/a-future-without-key-social-and-economic-statistics-for-the-country/].<br />
<br />
Submitted by Margaret Cibes</div>Simon66217https://www.causeweb.org/wiki/chance/index.php?title=Chance_News_85&diff=15946Chance News 852012-05-21T18:44:48Z<p>Simon66217: /* Forsooth */</p>
<hr />
<div>==Quotations==<br />
<br />
“Journalists could help people grasp uncertainty and help them apply critical thinking to health care decision-making issues…rather than promote false certainty, shibboleths and non-evidence-based, cheerleading advocacy.”<br />
<br />
<div align=right>-- Gary Schwitzer, at [http://www.healthnewsreview.org/2012/04/grasping-and-even-celebrating-uncertainty/ HealthNewsReview.org]</div><br />
<br />
"To treat your facts with imagination is one thing; to imagine your facts is another."<br />
<div align=right>-- John Burroughs (1837-1921), quoted in Gary's [http://www.slideshare.net/HealthNewsReview/uw-talk-for-slideshare talk] at the conference <br>[http://sciencedenial.wisc.edu/ Science Writing in the Age of Denial], University of Wisconsin, Madison </div><br />
<br />
Submitted by Paul Alper<br />
<br />
----<br />
"A big computer, a complex algorithm and a long time does not equal science." <br />
<br />
<div align=right>-- Robert Gentleman, quoted at [http://onertipaday.blogspot.com/ One R Tip A Day]</div><br />
Submitted by Bill Peterson<br />
----<br />
"Statistics [from observational studies] cannot turn sow's ears into silk purses, no matter how large the number of sow's ears available for study. Nor can adding up large numbers of scientifically impoverished studies yield scientific information. The appeal of statistics is that it is (a) very cheap compared to scientific testing, and (b) it can produce results to order because the data itself imposes relatively few constraints on the statistical conclusion drawn from it. Both of these render such methods irresistible to politicians and advocacy groups.”<br />
<div align=right>Stephen Krumpe blogging[http://online.wsj.com/article/SB10001424052702303916904577377841427001840.html?KEYWORDS=gautam+naik&_nocache=1336253792402&user=welcome&mg=id-wsj#articleTabs%3Dcomments] in response to<br><br />
[http://online.wsj.com/article/SB10001424052702303916904577377841427001840.html?KEYWORDS=gautam+naik&_nocache=1336253792402&user=welcome&mg=id-wsj “Analytical Trend Troubles Scientists”], <i>The Wall Street Journal</i>, May 4, 2012</div><br />
Submitted by Margaret Cibes<br />
<br />
----<br />
“The first principle [of scientific integrity] is that you must not fool yourself – and you are the easiest person to fool. …. I would like to add something that’s not essential to the science, but something I kind of believe, which is that you should not fool the layman when you’re talking as a scientist. ….One example of the principle is this: If you’ve made up your mind to test a theory, or you want to explain some idea, you should always decide to publish it whichever way it comes out. If we only publish results of a certain kind, we can make the argument look good. We must publish <i>both</i> kinds of results.”<br />
<br />
<div align=right>Richard Feynman, in [http://calteches.library.caltech.edu/3043/1/CargoCult.pdf “Cargo Cult Science”]<br><br />
Caltech’s 1974 commencement address</div><br />
Submitted by Margaret Cibes<br />
<br />
----<br />
From <i>Significance</i> magazine, April 2012:<br><br />
<br />
“It was a cause of great sorrow to me that I had absolutely no talent for the game [Liverpool football], or any sport other than chess, and I had to accept that I am a centre forward trapped inside a statistician’s body.”<br />
<div align=right>"Dr Fisher’s casebook: Sporting life"</div><br />
“We see an apparently unending upward spiral in remarkable levels of athletic achievement …. I think a major contributor to this remarkable increase in proficiency is population size. …. A simple statistical model that captures this idea posits that human running ability has not changed over the past century. That, in both 1900 and 2000 the distribution of running ability of the human race is well characterized by a normal curve with the same average and the same variability. What has changed is how many people live under that curve. …. [T]wo factors are working together. There is the growth of the total population of the world. There is also the (non-parallel) growth of the population who can participate. …. The best of a billion is likely better than the best of a million.”<br />
<div align=right>Howard Wainer in “Piano virtuosos and the four-minute mile”</div><br />
Submitted by Margaret Cibes<br />
<br />
==Forsooth==<br />
<br />
"We're spending $70 per person to fill this out. That’s just not cost effective," he continued, "especially since in the end this is not a scientific survey. It’s a random survey." Daniel Webster, first term Republican congressman from Florida, referring to the American Community Survey, as quoted in [http://www.nytimes.com/2012/05/20/sunday-review/the-debate-over-the-american-community-survey.html "The Beginning of the End of the Census?"] Catherin Rampell, The New York Times, May 19, 2012.<br />
<br />
==Fish oil==<br />
[http://well.blogs.nytimes.com/2012/04/11/weighing-the-evidence-on-fish-oils-for-heart-health/?ref=anahadoconnor&gwh=C0B11AE5752AA76BF96A15FA37421288 Weighing the evidence on fish oils for heart health]<br><br />
by Anahad O’Connor, Well blog, ''New York Times'', 11 April 2012<br />
<br />
According to O'Connor,<br />
<br />
<blockquote><br />
Fish oil supplements have become some of the most popular dietary pills on the market, largely on the strength of medical research linking diets high in baked and broiled fish to lower rates of heart disease. Across the United States, annual sales of purified fish oil, commonly sold as omega-3 fatty acids, are in the neighborhood of a billion dollars. And in some parts of Europe, doctors routinely prescribe fish oils to patients with heart disease.<br />
<br><br><br />
People who put their faith in fish oil supplements may want to reconsider. A new analysis of the evidence casts doubt on the widely touted notion that the pills can prevent heart attacks in people at risk for cardiovascular <br />
disease.<br />
</blockquote><br />
<br />
And well the people might. O’Connor is referring to [http://archinte.ama-assn.org/cgi/content/abstract/archinternmed.2012.262 “Efficacy of Omega-3 Fatty Acid Supplements (Eicosapentaenoic Acid and Docosahexaenoic Acid) in the Secondary Prevention of Cardiovascular Disease; A Meta-analysis of Randomized, Double-blind, Placebo-Controlled Trials”] by S.M. Kwak, et al., to appear in the ''Archives of Internal Medicine''. Not only did:<br />
<blockquote><br />
Our meta-analysis showed insufficient evidence of a secondary preventive effect of omega-3 fatty acid supplements against overall cardiovascular events among patients with a history of cardiovascular disease,<br />
</blockquote><br />
<br />
But also:<br />
<br />
<blockquote><br />
Furthermore, no significant preventive effect was observed in subgroup analyses by the following: country location, inland or coastal geographic area, history of CVD, concomitant medication use, type of placebo material in the trial, methodological quality of the trial, duration of treatment, dosage of eicosapentaenoic acid [EPA] or docosahexaenoic acid [DHA], or use of fish oil supplementation only as treatment.<br />
</blockquote><br />
<br />
'''Discussion'''<br />
<br />
1. The authors started their meta-analysis with 1007 articles; eventually, after 181 studies were excluded as duplicates and others were dropped out for various other reasons, they were left with “14 randomized, double blind, placebo-controlled trials.” The total number of subjects in the 14 trials was 20, 485. As stated above, statistical significance was not to be seen. Two large studies of 11,234 and 18, 645 subjects, respectively which did show beneficial effects from fish oil were not included in the 14; they were rejected because they were “open-label” studies. Why are open-label studies suspect?<br />
<br />
2. Why did the subjects in the placebo arm of the 14 studies receive various vegetable oils? Some of those subjects in the placebo arm received olive oil. Why might this “have disguised the ‘true’ benefit of omega-3 fatty acid supplementation?”<br />
<br />
3. If not fish oil, O’Connor says the authors conclude that<br />
<br />
<blockquote><br />
it may make the most sense to spend your money on actual fish, rather than fish oil supplements.<br />
<br ><br><br />
They argue that by eating fish, you end up replacing other less healthy protein sources, like processed foods and red meat. For that reason, a diet high in fatty fish — one that includes at least two servings a week — may make a difference over the long term, they say.<br />
</blockquote><br />
<br />
If the above is correct, why are so many people eschewing fish for fish oil?<br />
<br />
Submitted by Paul Alper<br />
<br />
==Choosing a spouse--really?==<br />
[http://www.nytimes.com/2012/05/06/magazine/romneys-former-bain-partner-makes-a-case-for-inequality.html?emc=eta1 The purpose of spectacular wealth, according to a spectacularly wealthy guy]<br><br />
by Adam Davidson, ''New York Times Magazine'', 1 May 2012<br />
<br />
Davidson describes an interview with Edward Conard, one of Mitt Romney's former associates from Bain Capital, who has written a book entitled ''Unintended Consequences: Why Everything You’ve Been Told About the Economy Is Wrong''. It is amusing to note the following, which appears that about halfway through the article:<br />
<blockquote><br />
There’s also the fact that Conard applies a relentless, mathematical logic to nearly everything, even finding a good spouse. He advocates, in utter seriousness, using demographic data to calculate the number of potential mates in your geographic area. Then, he says, you should set aside a bit of time for “calibration” — dating as many people as you can so that you have a sense of what the marriage marketplace is like. Then you enter the selection phase, this time with the goal of picking a permanent mate. The first woman you date who is a better match than the best woman you met during the calibration phase is, therefore, the person you should marry. By statistical probability, she is as good a match as you’re going to get. (Conard used this system himself.)<br />
</blockquote><br />
<br />
'''Discussion'''<br />
<br />
1. They almost got the description of the optimal strategy for the famous [http://en.wikipedia.org/wiki/Secretary_problem Secretary Problem] correct. What is missing from this argument?<br />
<br />
2. Do you believe that Conard actually used this system himself?<br />
<br />
Submitted by Charles Grinstead<br />
<br />
===Comments===<br />
1. This is a long article, which presents Conard's argument as a defense of unbridled, winner-take-all competition. He rejects the idea that income inequality is a problem; indeed, he thinks even greater rewards are needed as incentives for risk-taking entrepreneurs, whose efforts to boost the economy will benefit everyone. As a reminder that we have heard this before, Paul Alper sent the following quote from a previous century:<br />
<br />
<blockquote>The American Beauty Rose can be produced in the splendor and fragrance which bring cheer to its beholder only by sacrificing the early buds which grow up around it. This is not an evil tendency in business. It is merely the working-out of a law of nature and a law of God.</blockquote><br />
<div align=right>--John D. Rockefeller, address to the students of Brown University, quoted in Ida Tarbell (1904) ''The History of the Standard Oil Company''</div><br />
<br />
2. See also the post [http://economix.blogs.nytimes.com/2012/05/07/incentive-perversity/ Incentive perversity], Economix blog, ''New York Times'', 7 May 2012. University of Massachusetts economist Nancy Folbre reminds us that the links between rewards and performance are not so clear cut, and that incentives can be distorted when economic rewards grow too extreme. She points out that high-stakes educational testing led schools to cheat on exams, and out-sized contracts for star athletes led to an era tainted by performance-enhancing drugs. She concludes that, "Good incentives are always a good idea. But it’s not as easy to design them as it might seem, because they should discourage a host of economic sins — not just sloth and fear, but also cruelty and greed."<br />
<br />
==TV and the shortening of life==<br />
[http://bjsm.bmj.com/content/early/2011/08/01/bjsm.2011.085662.short?q=w_bjsm_ahead_tab Television viewing time and reduced life expectancy: a life table analysis]<br><br />
by J Lennert Veerman, et. al., ''British Journal of Sports Medicine'', 15 August 2011<br />
<br />
From the online abstract we read:<br />
<blockquote><br />
'''Results''' The amount of TV viewed in Australia in 2008 reduced life expectancy at birth by 1.8 years (95% uncertainty interval (UI): 8.4 days to 3.7 years) for men and 1.5 years (95% UI: 6.8 days to 3.1 years) for women. Compared with persons who watch no TV, those who spend a lifetime average of 6 h/day watching TV can expect to live 4.8 years (95% UI: 11 days to 10.4 years) less. On average, every single hour of TV viewed after the age of 25 reduces the viewer's life expectancy by 21.8 (95% UI: 0.3–44.7) min. This study is limited by the low precision with which the relationship between TV viewing time and mortality is currently known.<br />
<br><br><br />
'''Conclusions''' TV viewing time may be associated with a loss of life that is comparable to other major chronic disease risk factors such as physical inactivity and obesity.<br />
</blockquote><br />
<br />
Needless to say, this highly speculative--but very quotable--statistical analysis has been picked up by every conceivable web site since last August. It just made its appearance in the Minneapolis ''Star Tribune'': [http://www.startribune.com/lifestyle/150359955.html Can TV cut your life short?], by Jeff Strickler, 7 May 2012. The ''New York Times'' mentioned it a week earlier: [http://www.nytimes.com/2012/04/29/sunday-review/stand-up-for-fitness.html?_r=1&hp Don’t just sit there], by Gretchen Reynolds, 28 April 2012.<br />
<br />
Submitted by Paul Alper<br />
<br />
===Discussion===<br />
<br />
1. The NYT article describes a number of studies concerning the ill effects of inactivity. Their entire description of the Australian study reads, "researchers determined that watching an hour of television can snip 22 minutes from someone’s life. If an average man watched no TV in his adult life, the authors concluded, his life span might be 1.8 years longer, and a TV-less woman might live for a year and half longer than otherwise." What is missing here?<br />
<br />
2. Elsewhere in the story, however, the NYT notes that "Television viewing is a widely used measure of sedentary time." What does this suggest about interpreting the Australian study?<br />
<br />
=="Approval" statistic==<br />
[http://jonathanpelto.com/2012/05/07/memo-to-connecticut-democrats-only-all-others-should-skip-this-post/ “Memo to Connecticut Democrats”], by Jonathan Pelto, May 7, 2012<br><br />
<br />
From a CT blogger's website:<br />
<blockquote>The following chart indicates how Connecticut Democratic voters rate Governor Malloy’s job performance. In politics we use a statistic that measures the rate of approval compared to the rate of disapproval – we call that the overall positive or negative rating of an individual (i.e. +/-). The higher the positive rating the better the candidate or elected official is doing.</blockquote><br />
[[File:Malloy_polls.jpg]]<br />
===Questions===<br />
1. Can you think of a reason why the June 2011 poll figures sum to 106?<br><br />
2. The rightmost column heading might suggest that these figures are margins of error (percentage points), except for their size. If they had been margins of error, about how many people would have been in the sample on March 2011? Is that realistic?<br><br />
3. According to the text, the rightmost column contains a “statistic that measures the rate of approval compared to the rate of disapproval.” How do you think that the blogger compared approval/disapproval figures to come up with the figures in the rightmost column? (While I couldn’t find a definition of “approval rating,” I did found that the blogger's "statistic" is pretty common; for example, see Wikipedia's [http://en.wikipedia.org/wiki/United_States_presidential_approval_rating "United States presidential approval rating"].)<br><br />
4. How might you have entitled the +/- column, in order to clarify its meaning?<br><br />
5. The blogger opens the article by stating that Malloy's "support from members of [his] own party ... is at a breathtakingly low + 19 percent." Do you agree? What would you have said?<br><br />
<br />
Submitted by Margaret Cibes<br />
<br />
==Happiness and variability==<br />
[http://www.usatoday.com/news/health/wellness/story/2012-05-03/parents-happiness-population-survey/54767508/1? Parents today are happier than non-parents, studies suggest]<br><br />
by Sharon Jayson, ''USA Today'', 5 March 2012<br />
<br />
This article drew commentary on Andrew Gelman's blog ([http://andrewgelman.com/2012/05/happy-news-on-happiness-what-can-we-believe/ Happy news on happiness; what can we believe?], 7 May 2012). Gelman notes that the article concludes with the following quote: <blockquote>The first child increases happiness quite a lot. The second child a little. The third not at all.</blockquote> <br />
<br />
The quote is attributed to Mikko Myrskylä, the coauthor of [http://onlinelibrary.wiley.com/doi/10.1111/j.1728-4457.2011.00389.x/abstract A Global Perspective on Happiness and Fertility], one of the two studies described in the article.<br />
<br />
Gelman then complains, "As a statistician, I hate hate hate hate hate when people ignore variability and present results deterministically. The above statement might be an accurate summary of average patterns but is certainly not true in every case!"<br />
<br />
Submitted by Paul Alper<br />
<br />
==House cuts American Community Survey ==<br />
[http://www.nytimes.com/2012/05/14/opinion/operating-in-the-dark.html?_r=2 “Operating in the dark”], Editorial, <i>New York Times</i>, May 13, 2012<br><br />
[http://www.washingtonpost.com/blogs/ezra-klein/post/does-government-knowledge-mean-government-intrusion/2012/05/13/gIQAznUtMU_blog.html “Does government knowledge mean government intrusion?”], by Suzy Khimm,<i>Washington Post</i>, May 13, 2012<br><br />
<br />
Last week the U.S. House of Representatives voted to cut funds for the American Community Survey and at least part of the Economic Census. The former, “a bipartisan creation” in 2005, provides <i>annual</i> updates of economic, demographic and housing characteristics, which supplement the <i>decennial</i> census information. Let’s see what the Senate does ….<br />
<br />
See the U.S. Census Bureau Director’s statement (and video)[http://directorsblog.blogs.census.gov/2012/05/11/a-future-without-key-social-and-economic-statistics-for-the-country/].<br />
<br />
Submitted by Margaret Cibes</div>Simon66217https://www.causeweb.org/wiki/chance/index.php?title=Chance_News_83&diff=15454Chance News 832012-03-19T17:18:15Z<p>Simon66217: /* A bizarre anatomical correlation */</p>
<hr />
<div>==Quotations==<br />
“A poll is not laser surgery; it’s an estimate.”<br />
<div align=right>ABC News polling director in [http://abcnews.go.com/blogs/politics/2007/12/moe-and-mojo/ “MOE and Mojo”]<br><br />
ABC Blogs, December 3, 2007</div><br />
Submitted by Margaret Cibes<br />
<br />
----<br />
"The most famous result of Student’s experimental method is Student’s t-table. But the real end of Student’s inquiry was taste, quality control, and minimally efficient sample sizes for experimental Guinness – not to achieve statistical significance at the .05 level or, worse yet, boast about an artificially randomized experiment."<br />
<br />
<div align=right>--Stephen T. Ziliak, in [http://sites.roosevelt.edu/sziliak/files/2012/02/William-S-Gosset-and-Experimental-Statistics-Ziliak-JWE-2011.pdf W.S. Gosset and some neglected concepts in experimental statistics: Guinnessometrics II]</div><br />
<br />
(Ziliak is the co-author of [http://www.press.umich.edu/titleDetailPraise.do?id=186351 ''The Cult of Statistical Significance: How the Standard Error Costs Us Jobs, Justice, and Lives''])<br />
<br />
Submitted by Bill Peterson<br />
<br />
-----<br />
“[W. S. Gossett] wrote to R. A. Fisher of the t tables, "You are probably the only man who will ever use them (Box 1978)."<br><br />
<br />
“[W]e see the data analyst's insistence on ‘letting the data speak to us’ by plots and displays as an instinctive understanding of the need to encourage and to stimulate the pattern recognition and model generating capability of the right brain. Also, it expresses his concern that we not allow our pushy deductive left brain to take over too quickly and perhaps forcibly produce unwarranted conclusions based on an inadequate model.”<br />
<div align=right>George Box in “The Importance of Practice in the Development of Statistics”<br><br />
<i>Technometrics</i>, February 1984</div><br />
<br />
Thomas L. Moore recommended this article in an ISOSTAT posting. (It is available in [http://www.jstor.org/discover/10.2307/1268410?uid=3739952&uid=2129&uid=2134&uid=2&uid=70&uid=4&uid=3739256&sid=21100643590836 JSTOR].)<br />
<br />
----<br />
We are familiar with George Box’s famous statement: “All models are wrong but some are useful.” Here is another variant, cited in [http://en.wikiquote.org/wiki/George_E._P._Box Wikipedia]:<br />
:“Remember that all models are wrong; the practical question is how wrong do they have to be to not be useful.”<br />
<br />
Submitted by Margaret Cibes<br />
<br />
(Note. For an interesting discussion revisiting this theme, see [http://andrewgelman.com/2012/03/all-models-are-right-most-are-useless/ All models are right, most are useless] on Andrew Gelman's blog, 4 March 2012).<br />
----<br />
“There are two kinds of statistics: the kind you look up and the kind you make up.”<br />
<br />
<div align=right> --attributed to Rex Stout, American writer (1886 - 1975)</div><br />
Submitted by Paul Alper<br />
<br />
<br />
----<br />
<br />
“Definition of Statistics: The science of producing unreliable facts from reliable figures.”<br />
<br />
"The only science that enables different experts using the same figures to draw different conclusions."<br />
<br />
<div align=right> --attributed to Evan Esar, American humorist (1899–1995)</div><br />
Submitted by Paul Alper<br />
<br />
----<br />
“Science involves confronting our `absolute stupidity'. That kind of stupidity is an existential fact, inherent in our efforts to push our way into the unknown. …. Focusing on important questions puts us in the awkward position of being ignorant. One of the beautiful things about science is that it allows us to bumble along, getting it wrong time after time, and feel perfectly fine as long as we learn something each time. …. The more comfortable we become with being stupid, the deeper we will wade into the unknown and the more likely we are to make big discoveries.”<br />
<div align=right>UVa scientist Martin Schwartz in [http://jcs.biologists.org/content/121/11/1771.full “The importance of stupidity in scientific research”]<br><br />
<i>Journal of Cell Science</i>, 2008</div><br />
Submitted by Margaret Cibes<br />
<br />
==Forsooth==<br />
“In the first four months [at the new Resorts World Casino New York City], roughly 25,000 gamblers showed up every day, shoving a collective $2.3 billion through the slots and losing $140 million in the process. …. Resorts World offers more than 4,000 slot machines, but thanks to state law, there are no traditional card tables.”<br />
<div align=right>[http://online.wsj.com/article/SB10001424052970204792404577229481078006746.html?link=SM_clmst_sum#articleTabs%3Dcomments “The Gamblers’ New Game”]<br><br />
<i>The Wall Street Journal</i>, February 18, 2012</div><br />
Submitted by Margaret Cibes<br />
<br />
-----<br />
[[File:Elderly_drivers.jpg|400px]]<br><br><br />
“Drivers 85 and older still have a higher rate of deadly crashes than any other age group except teenagers.”<br><br />
<br />
(The article also describes two women who have learned to "compensate" for their macular degeneration in various ways - not necessarily welcome news!)<br />
<div align=right>[http://online.wsj.com/article/SB10001424052970204520204577251310179633818.html?KEYWORDS=sue+shellenbarger “Safer Over 70: Divers Keep the Keys”]<br><br />
<i>The Wall Street Journal</i>, February 29, 2012</div><br />
Submitted by Margaret Cibes<br />
<br />
-----<br />
[[File:PieChartMost.png|300px]]<br><br><br />
See the "Observation" at the top of the chart.<br />
<div align=right><br />
[http://junkcharts.typepad.com/junk_charts/2012/02/the-meaning-of-most.html “The Meaning of most”], downloaded from Junk Charts, March 1, 2012<br><br />
originally cited in [http://blog.kissmetrics.com/loading-time/?wide=1 “Mobile vs. Desktop”], KISSmetrics</div><br />
Submitted by Margaret Cibes<br />
<br />
-----<br />
<br />
“Here is the rub: Apple is so big, it’s running up against the law of large numbers. Also known as the golden theorem, with a proof attributed to the 17th-century Swiss mathematician Jacob Bernoulli, the law states that a variable will revert to a mean over a large sample of results. In the case of the largest companies, it suggests that high earnings growth and a rapid rise in share price will slow as those companies grow ever larger.”<br />
<div align=right>James Stewart in [http://www.nytimes.com/2012/02/25/business/apple-confronts-the-law-of-large-numbers-common-sense.html?_r=2&hp “Confronting a Law of Limits”]<br><br />
<i>The New York Times</i>, February 24, 2012</div><br />
Bill Peterson found Andrew Gelman’s comments[http://andrewgelman.com/2012/02/apple-confronts-the-law-of-large-numbers-huh/] about this article. <br />
<br />
Submitted by Margaret Cibes<br />
<br />
==Kaiser Fung on Minnesota’s ramp meters==<br />
<br />
A number references to Kaiser Fung’s book, ''Numbers Rule Your World'', appear in [http://test.causeweb.org/wiki/chance/index.php/Chance_News_82 Chance News 82]. From a Minnesotan’s point of view, however, the most important topic he discusses is not hurricanes, not drug testing, and not bias in standardized testing. Rather, the most critical issue is ramp metering as a means of improving traffic flow, relieving congestion and reducing travel time on Minnesota highways. “Industry experts regard Minnesota’s system of 430 ramp meters as a national model.”<br />
<br />
Unfortunately, “perception trumped reality.” An influential state senator, Dick Day, now a lobbyist for gambling interests, “led a charge to abolish the nationally recognized program, portraying it as part of the problem, not the solution.”<br />
<blockquote><br />
Leave it to Senator Day to speak the minds of “average Joes”--the people he meets at coffee shops, county fairs, summer parades, and the stock car races he loves. He saw ramp metering as a symbol of Big Government strangling our liberty.<br><br><br />
In the Twin Cities, drivers perceived their trip times to have lengthened [due to the ramp meters] even though in reality they have probably decreased. Thus, when in September 2000, the state legislature passed a mandate requiring MnDOT [Minnesota Department of Transportation] to conduct a “meters shutoff” experiment [of six weeks], the engineers [who devised the metering program] were stunned and disillusioned.<br />
</blockquote><br />
<br />
To make a long story short, when the ramp meters came back on, it turns out that: <br />
<blockquote><br />
[T]he engineering vision triumphed. Freeway conditions indeed worsened after the ramp meters went off. The key findings, based on actual measurements were as follows:<br />
*Peak freeway volume dropped by 9 percent.<br />
*Travel times rose by 22 percent, and the reliability deteriorated.<br />
*Travel speeds declined by 7 percent.<br />
*The number of crashes during merges jumped by 26 percent.<br />
</blockquote><br />
<br />
“The consultants further estimated that the benefits of ramp metering outweighed costs by five to one.” Nevertheless, the-above objective measures had to continue to battle subjective ones:<br />
<blockquote><br />
Despite the reality that commuters shortened their journeys if they waited their turns at the ramps, the drivers did not perceive the trade-off to be beneficial; they insisted that they would rather be moving slowly on the freeway than coming to a standstill at the ramp.<br />
</blockquote><br />
<br />
Accordingly, the engineers decided to modify the optimum solution to take into account driver psychology. “When they turned the lights back on, they limited waiting time on the ramps to four minutes, retired some unnecessary meters, and also shortened the operating hours.” Said differently, the constrained optimization model the engineers first considered left out some pivotal constraints.<br />
<br />
===Discussion===<br />
<br />
1. Do a search for “behavioral economics” to see the prevalence of irrational perceptions and subjective calculations in the economic sphere.<br />
<br />
2. Fung discusses an allied, albeit inverse, problem of waiting-time misconception. This instance concerns Disney World and its popular so-called FastPass as a means of avoiding queues. According to Fung<br />
<blockquote><br />
Clearly, FastPass users love the product--but how much waiting time can they save? Amazingly, the answer is none; they spend the same amount of time waiting for popular rides with or without FastPass!..So Disney confirms yet again that perception trumps reality. The FastPass concept is an absolute stroke of genius; it utterly changes perceived waiting times and has made many, many park-goers very, very giddy.<br />
</blockquote><br />
<br />
3. An oft-repeated and perhaps apocryphal operations research/statistics/decision theory anecdote has to do with elevators in a very large office building. Employees complained about excessive waiting times because the elevators all too frequently seemed to be in lockstep. Any physical solution such as creating a new elevator shaft or installing a complicated timing algorithm would be very expensive. The famous and utterly inexpensive psychological solution whereby perception trumped reality was to put in mirrors so that the waiting time would seem less because the employees would enjoy admiring themselves in the mirrors. Note that older and more benighted operations research/statistics/decision theory textbooks would have used the word “women” instead of “employees” in the previous sentence. <br />
<br />
4. A very modern and frustrating example of perception again trumping reality can often be observed in supermarkets which have installed self-checkout lanes without placing a limit on the number of items per shopper. In order to avoid a line at the regular checkout, some shoppers with an extremely large number of items will often choose the self-checkout and take much longer to finish than if had they queued at the regular checkout. Explain why said shoppers psychologically might prefer to persist in that behavior despite evidence to the contrary. Why don’t supermarkets simply limit the number of items per customer at self-checkout lanes?<br />
<br />
Submitted by Paul Alper<br />
<br />
==Don’t forget Chebyshev==<br />
<i>Super Crunchers</i>, by Ian Ayres, Random House, 2007<br><br />
<br />
<blockquote>When I taught at Stanford Law School, professors were required to award grades that had a 3.2 mean. …. The problem was that many of the students and many of the professors had no way to express the degree of variability in professors’ grading habits. …. As a nation, we lack a vocabulary of dispersion. We don’t know how to express what we intuitively know about the variability of a distribution of numbers. The 2SD [2 standard-deviation] rule could help give us this vocabulary. A professor who said that her standard deviation was .2 could have conveyed a lot of information with a single number. The problem is that very few people in the U.S. today understand what this means. But you should know and be able to explain to others that only about 2.5 percent of the professor’s grades are above 3.6. [pp. 221-222]</blockquote><br />
<br />
===Discussion===<br />
1. Suppose that a professor's <i>awarded</i> grades had mean 3.2 and SD 0.2.<br> <br />
(a) Under what condition could we say that “only about 2.5 percent of the professor’s grades are above 3.6”?<br><br />
(b) Without that condition, what could we say, if anything, about the percent of awarded grades <i>outside</i> of a 2SD range about the mean? About the percent of awarded grades <i>above</i> 3.6?<br><br />
2. Suppose that a professor's <i>raw grades</i> had mean 3.2 and SD 0.2. Do you think that this would be a realistic scenario in most undergraduate college classes? In most graduate-school classes? Why or why not?<br><br />
3. How could a professor construct a distribution of <i>awarded</i> grades with mean 3.2 and SD 0.2, based on <i>raw grades</i>, so that one could say that only about 2.5 percent of the <i>awarded</i> grades are above 3.6? What effect, if any, could that scaling have had on the worst – or on the best – raw grades?<br><br />
<br />
Submitted by Margaret Cibes<br />
<br />
==Critique of women-in-science statistics==<br />
[http://scholarship.claremont.edu/cgi/viewcontent.cgi?article=1041&context=jhm&sei-redir=1&referer=http%3A%2F%2Fwww.google.com%2Furl%3Fsa%3Dt%26rct%3Dj%26q%3Drumors%2520of%2520rarity%26source%3Dweb%26cd%3D1%26ved%3D0CCMQFjAA%26url%3Dhttp%253A%252F%252Fscholarship.claremont.edu%252Fcgi%252Fviewcontent.cgi%253Farticle%253D1041%2526context%253Djhm%26ei%3DugheT7arHMPV0QGzpfjkAw%26usg%3DAFQjCNGPJtXVTh10gldzpdqxaolp1Shd8Q#search=%22rumors%20rarity%22 “Rumors of Our Rarity are Greatly Exaggerated: Bad Statistics About Women in Science”]<br><br />
by Cathy Kessel, <i>Journal of Humanistic Mathematics</i>, July 2011<br><br />
<br />
Based on her apparently extensive and detailed study of reports about female-to-male ratios with respect to STEM abilities/careers, Kessel discusses three major problems with the statistics cited in them, as well as with the repetition of these questionable figures in subsequent academic and non-academic reports. <br />
<blockquote>Whatever their origins, statistics which are mislabeled, misinterpreted, fictitious, or otherwise defective remain in circulation because they are accepted by editors, readers, and referees.</blockquote><br />
<br />
“The Solitary Statistic.” A 13-to-1 boy-girl ratio in SAT-Math scores has been widely cited since it appeared in a 1983 <i>Science</i> article. That ratio was based on the scores of 280 seventh- and eighth-graders who scored 700 or above on the test over the period 1980-83. These students were part of a total of 64,000 students applying for a Johns Hopkins science program for exceptionally talented STEM-potential students. Kessel faults the widespread references to this outdated data, among other issues, and she cites more recent statistics at Hopkins and other such programs, including a ratio as low as 3 to 1 in 2005.<br><br />
<br />
“The Fabricated Statistic.” A “finding” that “Women talk almost three times as much as men” was published in <i>The Female Brain</i> in 2006. This was supposed to explain why women prefer careers which allow them to “connect and communicate” as opposed careers in science and engineering. Kessel outlines some issues that might make this explanation questionable.<br><br />
<br />
“The Garbled Statistic.” An example from “The Science of Sex Differences in Science and Mathematics,” published in <i>Psychological Science in the Public Interest</i> in 2007, was a report that women were “8.3% of tenure-track faculty at ‘elite’ mathematics departments.” A 2002 survey produced similar math data; that survey was based on the “top 50 departments.” These and other reports generally reported only the aggregate figure and not any of the raw data by rank. Kessel gives other examples in which raw data summary tables (which she had requested and received) would have been helpful to interpreting results.<br />
<blockquote>Although noticing mistakes may require numerical sophistication or knowledge of particular fields, accurate reporting of names, dates, and sources of<br />
statistics does not take much skill. At the very least, authors and research assistants can copy categories and sources as well as numbers. Editors can (and should) ask for sources.</blockquote><br />
===Discussion===<br />
1. Is there anything random about the group of students applying to a university’s program for talented students - or about the top SAT-M scorers in that group? Why are these important questions?<br><br />
2. Kessel quotes a statement that has been reported a number of times: “Women use 20,000 words per day, while men use 7,000." How do you think the researchers got these counts?<br><br />
3. Why might it be important to consider academic rank as a variable in analyzing the progress, or lack thereof, of women in obtaining university positions?<br><br />
4. Why might it be important to know more about the sponsorship of these studies – researcher affiliations, funding, <i>etc.</i>?<br><br />
<br />
Submitted by Margaret Cibes, based on a reference in March 2012 <i>College Mathematics Journal</i><br />
<br />
==Ethics study of social classes==<br />
[http://blogs.wsj.com/ideas-market/2012/02/27/study-high-social-class-predicts-unethical-behavior/tab/print/ “Study: High Social Class Predicts Unethical Behavior”]<br><br />
<i>The Wall Street Journal</i>, February 27, 2012<br><br />
<br />
Here is an abstract of the study[http://www.pnas.org/content/early/2012/02/21/1118373109.abstract] referred to in the article:<br />
<blockquote>Seven studies using experimental and naturalistic methods reveal that upper-class individuals behave more unethically than lower-class individuals. In studies 1 and 2, upper-class individuals were more likely to break the law while driving, relative to lower-class individuals. In follow-up laboratory studies, upper-class individuals were more likely to exhibit unethical decision-making tendencies (study 3), take valued goods from others (study 4), lie in a negotiation (study 5), cheat to increase their chances of winning a prize (study 6), and endorse unethical behavior at work (study 7) than were lower-class individuals. Mediator and moderator data demonstrated that upper-class individuals’ unethical tendencies are accounted for, in part, by their more favorable attitudes toward greed.</blockquote><br />
See also [http://www.pnas.org/content/suppl/2012/02/22/1118373109.DCSupplemental/pnas.201118373SI.pdf "Supporting Information"], published online in Proceedings of the National Academy of Sciences of the USA, February 27, 2012.<br><br />
<br />
===Discussion===<br />
1. If you were going to write an article about this study, and you had access to the entire report, what would be the first, most basic, information you would want to provide to your readers about the “class” categories referred to in the abstract?<br><br />
2. The article indicates that the sample sizes for the first three experiments were “250,” “150 drivers,” and “105 students.” Besides the relatively small sample sizes, what other issues can you identify as a potential problems in making any inference about ethics from these experimental results? <br />
<br />
Submitted by Margaret Cibes<br />
<br />
==Judea Pearl wins Turing Prize==<br />
Danny Kaplan posted a link to this story on the Isolated Statisicians e-mail list:<br />
<br />
[http://bits.blogs.nytimes.com/2012/03/15/a-turing-award-for-helping-make-computers-smarter/?hpw A Turing Award for helping make computers smarter].<br><br />
by Steve Lohr, Bits blog, ''New York Times'', 15 March 2012<br />
<br />
Judea Pearl of UCLA has been awarded this year's Turing Prize by the Association for Computing Machinery. According to the article Pearl's work on probabilistic reasoning and Bayesian networks has influenced applications in areas from search engines to fraud detection to speech recognition. The article includes testimonials from many noted experts in the field of artificial intelligence.<br />
<br />
Danny's message provided links to [http://bayes.cs.ucla.edu/jp_home.html Pearl's web page] for references to his work on causality, and to this [http://singapore.cs.ucla.edu/LECTURE/lecture_sec1.htm talk], which is the epilogue to his famous book, [http://bayes.cs.ucla.edu/BOOK-99/book-toc.html ''Causality''].<br />
<br />
Danny's [http://www.mosaic-web.org/go/StatisticalModeling ''Statistical Modeling'' textbook] includes a [http://www.mosaic-web.org/go/StatisticalModeling/Chapters/Chapter-17.pdf chapter] which discusses some of these ideas at a level appropriate for an introductory statistics audience.<br />
<br />
==A bizarre anatomical correlation==<br />
<br />
[http://www.nytimes.com/2012/03/17/opinion/collins-politicians-swinging-stethoscopes.html Politicians Swinging Stethoscopes], Gail Collins, The New York Times, March 16, 2012.<br />
<br />
When a topic carries strong emotions, often people forget to check their facts carefully. And abortion is possibly the most emotional topic in politics today. It's not too surprising that opponents of abortion have tried to promote a link between abortion and breast cancer.<br />
<br />
<blockquote>New Hampshire, for instance, seems to have developed a thing for linking sex and malignant disease. This week, the State House passed a bill that required that women who want to terminate a pregnancy be informed that abortions were linked to "an increased risk of breast cancer." As Terie Norelli, the minority leader, put it, the Legislature is attempting to make it a felony for a doctor "to not give a patient inaccurate information."</blockquote><br />
<br />
This was actually an issue [http://en.wikipedia.org/wiki/C._Everett_Koop#Government_service about 25 years ago, when C. Everett Koop was Surgeon General]. The American Cancer Society (ACS) has written that [http://www.cancer.org/Cancer/BreastCancer/MoreInformation/is-abortion-linked-to-breast-cancer scientific research studies have not found a cause-and-effect relationship between abortion and breast cancer], and cites a comprehensive review in 2003 by the National Cancer Institute (NCI). But numerous pro-life sites still claim the opposite, with headlines like [http://www.lifenews.com/2010/09/21/nat-6718/ Hundreds of Studies Confirm Abortion-Breast Cancer Link].<br />
<br />
It is interesting to speculate why pro-life sites would promote the abortion/breast cancer link so strongly in spite of dismissive commentary from respected organizations like ACS and NCI. If you believe that abortion is murder (as many people do), then it is not too far a leap to believe that something this evil would necessarily carry bad health consequences at the same time. It may also be a belief that mainstream organizations like ACS and NCI are dominated by [http://www.wnd.com/2010/01/121749/ pro-abortion extremists].<br />
<br />
It is still curious, because if abortion is immoral, it should not matter whether it has bad side effects, such as an increased risk of breast cancer, or good side effects, [http://en.wikipedia.org/wiki/The_Impact_of_Legalized_Abortion_on_Crime such as a decrease in the crime rate]. It may be that opponents of abortion feel free to use any weapon at their disposal to dissuade women from choosing abortion. It is worth noting that opponents of abortion are not the first and will not be the last group that has seized on a dubious statistical finding to support their political perspective.<br />
<br />
The abortion/breast cancer link at least has biological plausibility. The number of pregnancies that you have and the number of live births are indeed associated with various types of cancer, so it is not too far fetched to believe that abortion might be related to these cancers as well. But another cancer link in an area almost as contentious lacks even this biological plausibility.<br />
<br />
<blockquote>And there’s more. One of the sponsors, Representative Jeanine Notter, recently asked a colleague whether he would be interested, "as a man," to know that there was a study "that links the pill to prostate cancer."</blockquote><br />
<br />
Clearly, Ms. Notter understands that only women consume birth control pills and that only men have a prostate. What she is claiming is <br />
<br />
<blockquote>that nations with high use of birth control pills among women also tended to have high rates of prostate cancer among men.</blockquote><br />
<br />
Gail Collins mocks this correlation.<br />
<br />
<blockquote>You could also possibly discover that nations with the lowest per capita number of ferrets have a higher rate of prostate cancer.</blockquote><br />
<br />
Birth control pills are possibly associated with ovarian and cervical cancer, and these are two organs that women do have. They may also be associated with a decrease in the risk of endometrial cancer. If you don't believe that NCI is overrun by extremists, you might find [http://www.cancer.gov/cancertopics/factsheet/Risk/oral-contraceptives this fact sheet] to offer a helpful review of these risks. Disentangling these cancer risks from other confounders (such as the age at first sexual intercourse) is very difficult.<br />
<br />
Submitted by Steve Simon<br />
<br />
===Questions===<br />
<br />
1. What is the name for the type of study that notes that "nations with high use of birth control pills among women also tended to have high rates of prostate cancer among men"?<br />
<br />
2. Randomized studies of the link between abortion and breast cancer are clearly impossible. What types of observational studies might be used to examine this link? What are the strengths and weaknesses of those types of studies.</div>Simon66217https://www.causeweb.org/wiki/chance/index.php?title=Chance_News_83&diff=15453Chance News 832012-03-19T17:01:13Z<p>Simon66217: /* A bizarre anatomical correlation */</p>
<hr />
<div>==Quotations==<br />
“A poll is not laser surgery; it’s an estimate.”<br />
<div align=right>ABC News polling director in [http://abcnews.go.com/blogs/politics/2007/12/moe-and-mojo/ “MOE and Mojo”]<br><br />
ABC Blogs, December 3, 2007</div><br />
Submitted by Margaret Cibes<br />
<br />
----<br />
"The most famous result of Student’s experimental method is Student’s t-table. But the real end of Student’s inquiry was taste, quality control, and minimally efficient sample sizes for experimental Guinness – not to achieve statistical significance at the .05 level or, worse yet, boast about an artificially randomized experiment."<br />
<br />
<div align=right>--Stephen T. Ziliak, in [http://sites.roosevelt.edu/sziliak/files/2012/02/William-S-Gosset-and-Experimental-Statistics-Ziliak-JWE-2011.pdf W.S. Gosset and some neglected concepts in experimental statistics: Guinnessometrics II]</div><br />
<br />
(Ziliak is the co-author of [http://www.press.umich.edu/titleDetailPraise.do?id=186351 ''The Cult of Statistical Significance: How the Standard Error Costs Us Jobs, Justice, and Lives''])<br />
<br />
Submitted by Bill Peterson<br />
<br />
-----<br />
“[W. S. Gossett] wrote to R. A. Fisher of the t tables, "You are probably the only man who will ever use them (Box 1978)."<br><br />
<br />
“[W]e see the data analyst's insistence on ‘letting the data speak to us’ by plots and displays as an instinctive understanding of the need to encourage and to stimulate the pattern recognition and model generating capability of the right brain. Also, it expresses his concern that we not allow our pushy deductive left brain to take over too quickly and perhaps forcibly produce unwarranted conclusions based on an inadequate model.”<br />
<div align=right>George Box in “The Importance of Practice in the Development of Statistics”<br><br />
<i>Technometrics</i>, February 1984</div><br />
<br />
Thomas L. Moore recommended this article in an ISOSTAT posting. (It is available in [http://www.jstor.org/discover/10.2307/1268410?uid=3739952&uid=2129&uid=2134&uid=2&uid=70&uid=4&uid=3739256&sid=21100643590836 JSTOR].)<br />
<br />
----<br />
We are familiar with George Box’s famous statement: “All models are wrong but some are useful.” Here is another variant, cited in [http://en.wikiquote.org/wiki/George_E._P._Box Wikipedia]:<br />
:“Remember that all models are wrong; the practical question is how wrong do they have to be to not be useful.”<br />
<br />
Submitted by Margaret Cibes<br />
<br />
(Note. For an interesting discussion revisiting this theme, see [http://andrewgelman.com/2012/03/all-models-are-right-most-are-useless/ All models are right, most are useless] on Andrew Gelman's blog, 4 March 2012).<br />
----<br />
“There are two kinds of statistics: the kind you look up and the kind you make up.”<br />
<br />
<div align=right> --attributed to Rex Stout, American writer (1886 - 1975)</div><br />
Submitted by Paul Alper<br />
<br />
<br />
----<br />
<br />
“Definition of Statistics: The science of producing unreliable facts from reliable figures.”<br />
<br />
"The only science that enables different experts using the same figures to draw different conclusions."<br />
<br />
<div align=right> --attributed to Evan Esar, American humorist (1899–1995)</div><br />
Submitted by Paul Alper<br />
<br />
----<br />
“Science involves confronting our `absolute stupidity'. That kind of stupidity is an existential fact, inherent in our efforts to push our way into the unknown. …. Focusing on important questions puts us in the awkward position of being ignorant. One of the beautiful things about science is that it allows us to bumble along, getting it wrong time after time, and feel perfectly fine as long as we learn something each time. …. The more comfortable we become with being stupid, the deeper we will wade into the unknown and the more likely we are to make big discoveries.”<br />
<div align=right>UVa scientist Martin Schwartz in [http://jcs.biologists.org/content/121/11/1771.full “The importance of stupidity in scientific research”]<br><br />
<i>Journal of Cell Science</i>, 2008</div><br />
Submitted by Margaret Cibes<br />
<br />
==Forsooth==<br />
“In the first four months [at the new Resorts World Casino New York City], roughly 25,000 gamblers showed up every day, shoving a collective $2.3 billion through the slots and losing $140 million in the process. …. Resorts World offers more than 4,000 slot machines, but thanks to state law, there are no traditional card tables.”<br />
<div align=right>[http://online.wsj.com/article/SB10001424052970204792404577229481078006746.html?link=SM_clmst_sum#articleTabs%3Dcomments “The Gamblers’ New Game”]<br><br />
<i>The Wall Street Journal</i>, February 18, 2012</div><br />
Submitted by Margaret Cibes<br />
<br />
-----<br />
[[File:Elderly_drivers.jpg|400px]]<br><br><br />
“Drivers 85 and older still have a higher rate of deadly crashes than any other age group except teenagers.”<br><br />
<br />
(The article also describes two women who have learned to "compensate" for their macular degeneration in various ways - not necessarily welcome news!)<br />
<div align=right>[http://online.wsj.com/article/SB10001424052970204520204577251310179633818.html?KEYWORDS=sue+shellenbarger “Safer Over 70: Divers Keep the Keys”]<br><br />
<i>The Wall Street Journal</i>, February 29, 2012</div><br />
Submitted by Margaret Cibes<br />
<br />
-----<br />
[[File:PieChartMost.png|300px]]<br><br><br />
See the "Observation" at the top of the chart.<br />
<div align=right><br />
[http://junkcharts.typepad.com/junk_charts/2012/02/the-meaning-of-most.html “The Meaning of most”], downloaded from Junk Charts, March 1, 2012<br><br />
originally cited in [http://blog.kissmetrics.com/loading-time/?wide=1 “Mobile vs. Desktop”], KISSmetrics</div><br />
Submitted by Margaret Cibes<br />
<br />
-----<br />
<br />
“Here is the rub: Apple is so big, it’s running up against the law of large numbers. Also known as the golden theorem, with a proof attributed to the 17th-century Swiss mathematician Jacob Bernoulli, the law states that a variable will revert to a mean over a large sample of results. In the case of the largest companies, it suggests that high earnings growth and a rapid rise in share price will slow as those companies grow ever larger.”<br />
<div align=right>James Stewart in [http://www.nytimes.com/2012/02/25/business/apple-confronts-the-law-of-large-numbers-common-sense.html?_r=2&hp “Confronting a Law of Limits”]<br><br />
<i>The New York Times</i>, February 24, 2012</div><br />
Bill Peterson found Andrew Gelman’s comments[http://andrewgelman.com/2012/02/apple-confronts-the-law-of-large-numbers-huh/] about this article. <br />
<br />
Submitted by Margaret Cibes<br />
<br />
==Kaiser Fung on Minnesota’s ramp meters==<br />
<br />
A number references to Kaiser Fung’s book, ''Numbers Rule Your World'', appear in [http://test.causeweb.org/wiki/chance/index.php/Chance_News_82 Chance News 82]. From a Minnesotan’s point of view, however, the most important topic he discusses is not hurricanes, not drug testing, and not bias in standardized testing. Rather, the most critical issue is ramp metering as a means of improving traffic flow, relieving congestion and reducing travel time on Minnesota highways. “Industry experts regard Minnesota’s system of 430 ramp meters as a national model.”<br />
<br />
Unfortunately, “perception trumped reality.” An influential state senator, Dick Day, now a lobbyist for gambling interests, “led a charge to abolish the nationally recognized program, portraying it as part of the problem, not the solution.”<br />
<blockquote><br />
Leave it to Senator Day to speak the minds of “average Joes”--the people he meets at coffee shops, county fairs, summer parades, and the stock car races he loves. He saw ramp metering as a symbol of Big Government strangling our liberty.<br><br><br />
In the Twin Cities, drivers perceived their trip times to have lengthened [due to the ramp meters] even though in reality they have probably decreased. Thus, when in September 2000, the state legislature passed a mandate requiring MnDOT [Minnesota Department of Transportation] to conduct a “meters shutoff” experiment [of six weeks], the engineers [who devised the metering program] were stunned and disillusioned.<br />
</blockquote><br />
<br />
To make a long story short, when the ramp meters came back on, it turns out that: <br />
<blockquote><br />
[T]he engineering vision triumphed. Freeway conditions indeed worsened after the ramp meters went off. The key findings, based on actual measurements were as follows:<br />
*Peak freeway volume dropped by 9 percent.<br />
*Travel times rose by 22 percent, and the reliability deteriorated.<br />
*Travel speeds declined by 7 percent.<br />
*The number of crashes during merges jumped by 26 percent.<br />
</blockquote><br />
<br />
“The consultants further estimated that the benefits of ramp metering outweighed costs by five to one.” Nevertheless, the-above objective measures had to continue to battle subjective ones:<br />
<blockquote><br />
Despite the reality that commuters shortened their journeys if they waited their turns at the ramps, the drivers did not perceive the trade-off to be beneficial; they insisted that they would rather be moving slowly on the freeway than coming to a standstill at the ramp.<br />
</blockquote><br />
<br />
Accordingly, the engineers decided to modify the optimum solution to take into account driver psychology. “When they turned the lights back on, they limited waiting time on the ramps to four minutes, retired some unnecessary meters, and also shortened the operating hours.” Said differently, the constrained optimization model the engineers first considered left out some pivotal constraints.<br />
<br />
===Discussion===<br />
<br />
1. Do a search for “behavioral economics” to see the prevalence of irrational perceptions and subjective calculations in the economic sphere.<br />
<br />
2. Fung discusses an allied, albeit inverse, problem of waiting-time misconception. This instance concerns Disney World and its popular so-called FastPass as a means of avoiding queues. According to Fung<br />
<blockquote><br />
Clearly, FastPass users love the product--but how much waiting time can they save? Amazingly, the answer is none; they spend the same amount of time waiting for popular rides with or without FastPass!..So Disney confirms yet again that perception trumps reality. The FastPass concept is an absolute stroke of genius; it utterly changes perceived waiting times and has made many, many park-goers very, very giddy.<br />
</blockquote><br />
<br />
3. An oft-repeated and perhaps apocryphal operations research/statistics/decision theory anecdote has to do with elevators in a very large office building. Employees complained about excessive waiting times because the elevators all too frequently seemed to be in lockstep. Any physical solution such as creating a new elevator shaft or installing a complicated timing algorithm would be very expensive. The famous and utterly inexpensive psychological solution whereby perception trumped reality was to put in mirrors so that the waiting time would seem less because the employees would enjoy admiring themselves in the mirrors. Note that older and more benighted operations research/statistics/decision theory textbooks would have used the word “women” instead of “employees” in the previous sentence. <br />
<br />
4. A very modern and frustrating example of perception again trumping reality can often be observed in supermarkets which have installed self-checkout lanes without placing a limit on the number of items per shopper. In order to avoid a line at the regular checkout, some shoppers with an extremely large number of items will often choose the self-checkout and take much longer to finish than if had they queued at the regular checkout. Explain why said shoppers psychologically might prefer to persist in that behavior despite evidence to the contrary. Why don’t supermarkets simply limit the number of items per customer at self-checkout lanes?<br />
<br />
Submitted by Paul Alper<br />
<br />
==Don’t forget Chebyshev==<br />
<i>Super Crunchers</i>, by Ian Ayres, Random House, 2007<br><br />
<br />
<blockquote>When I taught at Stanford Law School, professors were required to award grades that had a 3.2 mean. …. The problem was that many of the students and many of the professors had no way to express the degree of variability in professors’ grading habits. …. As a nation, we lack a vocabulary of dispersion. We don’t know how to express what we intuitively know about the variability of a distribution of numbers. The 2SD [2 standard-deviation] rule could help give us this vocabulary. A professor who said that her standard deviation was .2 could have conveyed a lot of information with a single number. The problem is that very few people in the U.S. today understand what this means. But you should know and be able to explain to others that only about 2.5 percent of the professor’s grades are above 3.6. [pp. 221-222]</blockquote><br />
<br />
===Discussion===<br />
1. Suppose that a professor's <i>awarded</i> grades had mean 3.2 and SD 0.2.<br> <br />
(a) Under what condition could we say that “only about 2.5 percent of the professor’s grades are above 3.6”?<br><br />
(b) Without that condition, what could we say, if anything, about the percent of awarded grades <i>outside</i> of a 2SD range about the mean? About the percent of awarded grades <i>above</i> 3.6?<br><br />
2. Suppose that a professor's <i>raw grades</i> had mean 3.2 and SD 0.2. Do you think that this would be a realistic scenario in most undergraduate college classes? In most graduate-school classes? Why or why not?<br><br />
3. How could a professor construct a distribution of <i>awarded</i> grades with mean 3.2 and SD 0.2, based on <i>raw grades</i>, so that one could say that only about 2.5 percent of the <i>awarded</i> grades are above 3.6? What effect, if any, could that scaling have had on the worst – or on the best – raw grades?<br><br />
<br />
Submitted by Margaret Cibes<br />
<br />
==Critique of women-in-science statistics==<br />
[http://scholarship.claremont.edu/cgi/viewcontent.cgi?article=1041&context=jhm&sei-redir=1&referer=http%3A%2F%2Fwww.google.com%2Furl%3Fsa%3Dt%26rct%3Dj%26q%3Drumors%2520of%2520rarity%26source%3Dweb%26cd%3D1%26ved%3D0CCMQFjAA%26url%3Dhttp%253A%252F%252Fscholarship.claremont.edu%252Fcgi%252Fviewcontent.cgi%253Farticle%253D1041%2526context%253Djhm%26ei%3DugheT7arHMPV0QGzpfjkAw%26usg%3DAFQjCNGPJtXVTh10gldzpdqxaolp1Shd8Q#search=%22rumors%20rarity%22 “Rumors of Our Rarity are Greatly Exaggerated: Bad Statistics About Women in Science”]<br><br />
by Cathy Kessel, <i>Journal of Humanistic Mathematics</i>, July 2011<br><br />
<br />
Based on her apparently extensive and detailed study of reports about female-to-male ratios with respect to STEM abilities/careers, Kessel discusses three major problems with the statistics cited in them, as well as with the repetition of these questionable figures in subsequent academic and non-academic reports. <br />
<blockquote>Whatever their origins, statistics which are mislabeled, misinterpreted, fictitious, or otherwise defective remain in circulation because they are accepted by editors, readers, and referees.</blockquote><br />
<br />
“The Solitary Statistic.” A 13-to-1 boy-girl ratio in SAT-Math scores has been widely cited since it appeared in a 1983 <i>Science</i> article. That ratio was based on the scores of 280 seventh- and eighth-graders who scored 700 or above on the test over the period 1980-83. These students were part of a total of 64,000 students applying for a Johns Hopkins science program for exceptionally talented STEM-potential students. Kessel faults the widespread references to this outdated data, among other issues, and she cites more recent statistics at Hopkins and other such programs, including a ratio as low as 3 to 1 in 2005.<br><br />
<br />
“The Fabricated Statistic.” A “finding” that “Women talk almost three times as much as men” was published in <i>The Female Brain</i> in 2006. This was supposed to explain why women prefer careers which allow them to “connect and communicate” as opposed careers in science and engineering. Kessel outlines some issues that might make this explanation questionable.<br><br />
<br />
“The Garbled Statistic.” An example from “The Science of Sex Differences in Science and Mathematics,” published in <i>Psychological Science in the Public Interest</i> in 2007, was a report that women were “8.3% of tenure-track faculty at ‘elite’ mathematics departments.” A 2002 survey produced similar math data; that survey was based on the “top 50 departments.” These and other reports generally reported only the aggregate figure and not any of the raw data by rank. Kessel gives other examples in which raw data summary tables (which she had requested and received) would have been helpful to interpreting results.<br />
<blockquote>Although noticing mistakes may require numerical sophistication or knowledge of particular fields, accurate reporting of names, dates, and sources of<br />
statistics does not take much skill. At the very least, authors and research assistants can copy categories and sources as well as numbers. Editors can (and should) ask for sources.</blockquote><br />
===Discussion===<br />
1. Is there anything random about the group of students applying to a university’s program for talented students - or about the top SAT-M scorers in that group? Why are these important questions?<br><br />
2. Kessel quotes a statement that has been reported a number of times: “Women use 20,000 words per day, while men use 7,000." How do you think the researchers got these counts?<br><br />
3. Why might it be important to consider academic rank as a variable in analyzing the progress, or lack thereof, of women in obtaining university positions?<br><br />
4. Why might it be important to know more about the sponsorship of these studies – researcher affiliations, funding, <i>etc.</i>?<br><br />
<br />
Submitted by Margaret Cibes, based on a reference in March 2012 <i>College Mathematics Journal</i><br />
<br />
==Ethics study of social classes==<br />
[http://blogs.wsj.com/ideas-market/2012/02/27/study-high-social-class-predicts-unethical-behavior/tab/print/ “Study: High Social Class Predicts Unethical Behavior”]<br><br />
<i>The Wall Street Journal</i>, February 27, 2012<br><br />
<br />
Here is an abstract of the study[http://www.pnas.org/content/early/2012/02/21/1118373109.abstract] referred to in the article:<br />
<blockquote>Seven studies using experimental and naturalistic methods reveal that upper-class individuals behave more unethically than lower-class individuals. In studies 1 and 2, upper-class individuals were more likely to break the law while driving, relative to lower-class individuals. In follow-up laboratory studies, upper-class individuals were more likely to exhibit unethical decision-making tendencies (study 3), take valued goods from others (study 4), lie in a negotiation (study 5), cheat to increase their chances of winning a prize (study 6), and endorse unethical behavior at work (study 7) than were lower-class individuals. Mediator and moderator data demonstrated that upper-class individuals’ unethical tendencies are accounted for, in part, by their more favorable attitudes toward greed.</blockquote><br />
See also [http://www.pnas.org/content/suppl/2012/02/22/1118373109.DCSupplemental/pnas.201118373SI.pdf "Supporting Information"], published online in Proceedings of the National Academy of Sciences of the USA, February 27, 2012.<br><br />
<br />
===Discussion===<br />
1. If you were going to write an article about this study, and you had access to the entire report, what would be the first, most basic, information you would want to provide to your readers about the “class” categories referred to in the abstract?<br><br />
2. The article indicates that the sample sizes for the first three experiments were “250,” “150 drivers,” and “105 students.” Besides the relatively small sample sizes, what other issues can you identify as a potential problems in making any inference about ethics from these experimental results? <br />
<br />
Submitted by Margaret Cibes<br />
<br />
==Judea Pearl wins Turing Prize==<br />
Danny Kaplan posted a link to this story on the Isolated Statisicians e-mail list:<br />
<br />
[http://bits.blogs.nytimes.com/2012/03/15/a-turing-award-for-helping-make-computers-smarter/?hpw A Turing Award for helping make computers smarter].<br><br />
by Steve Lohr, Bits blog, ''New York Times'', 15 March 2012<br />
<br />
Judea Pearl of UCLA has been awarded this year's Turing Prize by the Association for Computing Machinery. According to the article Pearl's work on probabilistic reasoning and Bayesian networks has influenced applications in areas from search engines to fraud detection to speech recognition. The article includes testimonials from many noted experts in the field of artificial intelligence.<br />
<br />
Danny's message provided links to [http://bayes.cs.ucla.edu/jp_home.html Pearl's web page] for references to his work on causality, and to this [http://singapore.cs.ucla.edu/LECTURE/lecture_sec1.htm talk], which is the epilogue to his famous book, [http://bayes.cs.ucla.edu/BOOK-99/book-toc.html ''Causality''].<br />
<br />
Danny's [http://www.mosaic-web.org/go/StatisticalModeling ''Statistical Modeling'' textbook] includes a [http://www.mosaic-web.org/go/StatisticalModeling/Chapters/Chapter-17.pdf chapter] which discusses some of these ideas at a level appropriate for an introductory statistics audience.<br />
<br />
==A bizarre anatomical correlation==<br />
<br />
[http://www.nytimes.com/2012/03/17/opinion/collins-politicians-swinging-stethoscopes.html Politicians Swinging Stethoscopes], Gail Collins, The New York Times, March 16, 2012.<br />
<br />
When a topic carries strong emotions, often people forget to check their facts carefully. And abortion is possibly the most emotional topic in politics today. It's not too surprising that opponents of abortion have tried to promote a link between abortion and breast cancer.<br />
<br />
<blockquote>New Hampshire, for instance, seems to have developed a thing for linking sex and malignant disease. This week, the State House passed a bill that required that women who want to terminate a pregnancy be informed that abortions were linked to "an increased risk of breast cancer." As Terie Norelli, the minority leader, put it, the Legislature is attempting to make it a felony for a doctor "to not give a patient inaccurate information."</blockquote><br />
<br />
This was actually an issue [http://en.wikipedia.org/wiki/C._Everett_Koop#Government_service about 25 years ago, when C. Everett Koop was Surgeon General]. The American Cancer Society (ACS) has written that [http://www.cancer.org/Cancer/BreastCancer/MoreInformation/is-abortion-linked-to-breast-cancer scientific research studies have not found a cause-and-effect relationship between abortion and breast cancer], and cites a comprehensive review in 2003 by the National Cancer Institute (NCI). But numerous pro-life sites still claim the opposite, with headlines like [http://www.lifenews.com/2010/09/21/nat-6718/ Hundreds of Studies Confirm Abortion-Breast Cancer Link].<br />
<br />
It is interesting to speculate why pro-life sites would promote the abortion/breast cancer link so strongly in spite of dismissive commentary from respected organizations like ACS and NCI. If you believe that abortion is murder (as many people do), then it is not too far a leap to believe that something this evil would necessarily carry bad health consequences at the same time. It may also be a belief that mainstream organizations like ACS and NCI are dominated by [http://www.wnd.com/2010/01/121749/ pro-abortion extremists].<br />
<br />
It is still curious, because if abortion is immoral, it should not matter whether it has bad side effects, such as an increased risk of breast cancer, or good side effects, [http://en.wikipedia.org/wiki/The_Impact_of_Legalized_Abortion_on_Crime such as a decrease in the crime rate]. It may be that opponents of abortion feel free to use any weapon at their disposal to dissuade women from choosing abortion. It is worth noting that opponents of abortion are not the first and will not be the last group that has seized on a dubious statistical finding to support their political perspective.<br />
<br />
The abortion/breast cancer link at least has biological plausibility. The number of pregnancies that you have and the number of live births are indeed associated with various types of cancer, so it is not too far fetched to believe that abortion might be related to these cancers as well. But another cancer link in an area almost as contentious lacks even this biological plausibility.<br />
<br />
<blockquote>And there’s more. One of the sponsors, Representative Jeanine Notter, recently asked a colleague whether he would be interested, "as a man," to know that there was a study "that links the pill to prostate cancer."</blockquote><br />
<br />
Clearly, Ms. Notter understands that only women consume birth control pills and that only men have a prostate. What she is claiming is <br />
<br />
<blockquote>that nations with high use of birth control pills among women also tended to have high rates of prostate cancer among men.</blockquote><br />
<br />
Gail Collins mocks this correlation.<br />
<br />
<blockquote>You could also possibly discover that nations with the lowest per capita number of ferrets have a higher rate of prostate cancer.</blockquote><br />
<br />
Birth control pills are possibly associated with ovarian and cervical cancer, and these are two organs that women do have. They may also be associated with a decrease in the risk of endometrial cancer. If you don't believe that NCI is overrun by extremists, you might find [http://www.cancer.gov/cancertopics/factsheet/Risk/oral-contraceptives this fact sheet] to offer a helpful review of these risks. Disentangling these cancer risks from other confounders (such as the age at first sexual intercourse) is very difficult.<br />
<br />
===Questions===<br />
<br />
1. What is the name for the type of study that notes that "nations with high use of birth control pills among women also tended to have high rates of prostate cancer among men"?<br />
<br />
2. Randomized studies of the link between abortion and breast cancer are clearly impossible. What types of observational studies might be used to examine this link? What are the strengths and weaknesses of those types of studies.</div>Simon66217https://www.causeweb.org/wiki/chance/index.php?title=Chance_News_83&diff=15452Chance News 832012-03-19T16:57:40Z<p>Simon66217: /* Questions */</p>
<hr />
<div>==Quotations==<br />
“A poll is not laser surgery; it’s an estimate.”<br />
<div align=right>ABC News polling director in [http://abcnews.go.com/blogs/politics/2007/12/moe-and-mojo/ “MOE and Mojo”]<br><br />
ABC Blogs, December 3, 2007</div><br />
Submitted by Margaret Cibes<br />
<br />
----<br />
"The most famous result of Student’s experimental method is Student’s t-table. But the real end of Student’s inquiry was taste, quality control, and minimally efficient sample sizes for experimental Guinness – not to achieve statistical significance at the .05 level or, worse yet, boast about an artificially randomized experiment."<br />
<br />
<div align=right>--Stephen T. Ziliak, in [http://sites.roosevelt.edu/sziliak/files/2012/02/William-S-Gosset-and-Experimental-Statistics-Ziliak-JWE-2011.pdf W.S. Gosset and some neglected concepts in experimental statistics: Guinnessometrics II]</div><br />
<br />
(Ziliak is the co-author of [http://www.press.umich.edu/titleDetailPraise.do?id=186351 ''The Cult of Statistical Significance: How the Standard Error Costs Us Jobs, Justice, and Lives''])<br />
<br />
Submitted by Bill Peterson<br />
<br />
-----<br />
“[W. S. Gossett] wrote to R. A. Fisher of the t tables, "You are probably the only man who will ever use them (Box 1978)."<br><br />
<br />
“[W]e see the data analyst's insistence on ‘letting the data speak to us’ by plots and displays as an instinctive understanding of the need to encourage and to stimulate the pattern recognition and model generating capability of the right brain. Also, it expresses his concern that we not allow our pushy deductive left brain to take over too quickly and perhaps forcibly produce unwarranted conclusions based on an inadequate model.”<br />
<div align=right>George Box in “The Importance of Practice in the Development of Statistics”<br><br />
<i>Technometrics</i>, February 1984</div><br />
<br />
Thomas L. Moore recommended this article in an ISOSTAT posting. (It is available in [http://www.jstor.org/discover/10.2307/1268410?uid=3739952&uid=2129&uid=2134&uid=2&uid=70&uid=4&uid=3739256&sid=21100643590836 JSTOR].)<br />
<br />
----<br />
We are familiar with George Box’s famous statement: “All models are wrong but some are useful.” Here is another variant, cited in [http://en.wikiquote.org/wiki/George_E._P._Box Wikipedia]:<br />
:“Remember that all models are wrong; the practical question is how wrong do they have to be to not be useful.”<br />
<br />
Submitted by Margaret Cibes<br />
<br />
(Note. For an interesting discussion revisiting this theme, see [http://andrewgelman.com/2012/03/all-models-are-right-most-are-useless/ All models are right, most are useless] on Andrew Gelman's blog, 4 March 2012).<br />
----<br />
“There are two kinds of statistics: the kind you look up and the kind you make up.”<br />
<br />
<div align=right> --attributed to Rex Stout, American writer (1886 - 1975)</div><br />
Submitted by Paul Alper<br />
<br />
<br />
----<br />
<br />
“Definition of Statistics: The science of producing unreliable facts from reliable figures.”<br />
<br />
"The only science that enables different experts using the same figures to draw different conclusions."<br />
<br />
<div align=right> --attributed to Evan Esar, American humorist (1899–1995)</div><br />
Submitted by Paul Alper<br />
<br />
----<br />
“Science involves confronting our `absolute stupidity'. That kind of stupidity is an existential fact, inherent in our efforts to push our way into the unknown. …. Focusing on important questions puts us in the awkward position of being ignorant. One of the beautiful things about science is that it allows us to bumble along, getting it wrong time after time, and feel perfectly fine as long as we learn something each time. …. The more comfortable we become with being stupid, the deeper we will wade into the unknown and the more likely we are to make big discoveries.”<br />
<div align=right>UVa scientist Martin Schwartz in [http://jcs.biologists.org/content/121/11/1771.full “The importance of stupidity in scientific research”]<br><br />
<i>Journal of Cell Science</i>, 2008</div><br />
Submitted by Margaret Cibes<br />
<br />
==Forsooth==<br />
“In the first four months [at the new Resorts World Casino New York City], roughly 25,000 gamblers showed up every day, shoving a collective $2.3 billion through the slots and losing $140 million in the process. …. Resorts World offers more than 4,000 slot machines, but thanks to state law, there are no traditional card tables.”<br />
<div align=right>[http://online.wsj.com/article/SB10001424052970204792404577229481078006746.html?link=SM_clmst_sum#articleTabs%3Dcomments “The Gamblers’ New Game”]<br><br />
<i>The Wall Street Journal</i>, February 18, 2012</div><br />
Submitted by Margaret Cibes<br />
<br />
-----<br />
[[File:Elderly_drivers.jpg|400px]]<br><br><br />
“Drivers 85 and older still have a higher rate of deadly crashes than any other age group except teenagers.”<br><br />
<br />
(The article also describes two women who have learned to "compensate" for their macular degeneration in various ways - not necessarily welcome news!)<br />
<div align=right>[http://online.wsj.com/article/SB10001424052970204520204577251310179633818.html?KEYWORDS=sue+shellenbarger “Safer Over 70: Divers Keep the Keys”]<br><br />
<i>The Wall Street Journal</i>, February 29, 2012</div><br />
Submitted by Margaret Cibes<br />
<br />
-----<br />
[[File:PieChartMost.png|300px]]<br><br><br />
See the "Observation" at the top of the chart.<br />
<div align=right><br />
[http://junkcharts.typepad.com/junk_charts/2012/02/the-meaning-of-most.html “The Meaning of most”], downloaded from Junk Charts, March 1, 2012<br><br />
originally cited in [http://blog.kissmetrics.com/loading-time/?wide=1 “Mobile vs. Desktop”], KISSmetrics</div><br />
Submitted by Margaret Cibes<br />
<br />
-----<br />
<br />
“Here is the rub: Apple is so big, it’s running up against the law of large numbers. Also known as the golden theorem, with a proof attributed to the 17th-century Swiss mathematician Jacob Bernoulli, the law states that a variable will revert to a mean over a large sample of results. In the case of the largest companies, it suggests that high earnings growth and a rapid rise in share price will slow as those companies grow ever larger.”<br />
<div align=right>James Stewart in [http://www.nytimes.com/2012/02/25/business/apple-confronts-the-law-of-large-numbers-common-sense.html?_r=2&hp “Confronting a Law of Limits”]<br><br />
<i>The New York Times</i>, February 24, 2012</div><br />
Bill Peterson found Andrew Gelman’s comments[http://andrewgelman.com/2012/02/apple-confronts-the-law-of-large-numbers-huh/] about this article. <br />
<br />
Submitted by Margaret Cibes<br />
<br />
==Kaiser Fung on Minnesota’s ramp meters==<br />
<br />
A number references to Kaiser Fung’s book, ''Numbers Rule Your World'', appear in [http://test.causeweb.org/wiki/chance/index.php/Chance_News_82 Chance News 82]. From a Minnesotan’s point of view, however, the most important topic he discusses is not hurricanes, not drug testing, and not bias in standardized testing. Rather, the most critical issue is ramp metering as a means of improving traffic flow, relieving congestion and reducing travel time on Minnesota highways. “Industry experts regard Minnesota’s system of 430 ramp meters as a national model.”<br />
<br />
Unfortunately, “perception trumped reality.” An influential state senator, Dick Day, now a lobbyist for gambling interests, “led a charge to abolish the nationally recognized program, portraying it as part of the problem, not the solution.”<br />
<blockquote><br />
Leave it to Senator Day to speak the minds of “average Joes”--the people he meets at coffee shops, county fairs, summer parades, and the stock car races he loves. He saw ramp metering as a symbol of Big Government strangling our liberty.<br><br><br />
In the Twin Cities, drivers perceived their trip times to have lengthened [due to the ramp meters] even though in reality they have probably decreased. Thus, when in September 2000, the state legislature passed a mandate requiring MnDOT [Minnesota Department of Transportation] to conduct a “meters shutoff” experiment [of six weeks], the engineers [who devised the metering program] were stunned and disillusioned.<br />
</blockquote><br />
<br />
To make a long story short, when the ramp meters came back on, it turns out that: <br />
<blockquote><br />
[T]he engineering vision triumphed. Freeway conditions indeed worsened after the ramp meters went off. The key findings, based on actual measurements were as follows:<br />
*Peak freeway volume dropped by 9 percent.<br />
*Travel times rose by 22 percent, and the reliability deteriorated.<br />
*Travel speeds declined by 7 percent.<br />
*The number of crashes during merges jumped by 26 percent.<br />
</blockquote><br />
<br />
“The consultants further estimated that the benefits of ramp metering outweighed costs by five to one.” Nevertheless, the-above objective measures had to continue to battle subjective ones:<br />
<blockquote><br />
Despite the reality that commuters shortened their journeys if they waited their turns at the ramps, the drivers did not perceive the trade-off to be beneficial; they insisted that they would rather be moving slowly on the freeway than coming to a standstill at the ramp.<br />
</blockquote><br />
<br />
Accordingly, the engineers decided to modify the optimum solution to take into account driver psychology. “When they turned the lights back on, they limited waiting time on the ramps to four minutes, retired some unnecessary meters, and also shortened the operating hours.” Said differently, the constrained optimization model the engineers first considered left out some pivotal constraints.<br />
<br />
===Discussion===<br />
<br />
1. Do a search for “behavioral economics” to see the prevalence of irrational perceptions and subjective calculations in the economic sphere.<br />
<br />
2. Fung discusses an allied, albeit inverse, problem of waiting-time misconception. This instance concerns Disney World and its popular so-called FastPass as a means of avoiding queues. According to Fung<br />
<blockquote><br />
Clearly, FastPass users love the product--but how much waiting time can they save? Amazingly, the answer is none; they spend the same amount of time waiting for popular rides with or without FastPass!..So Disney confirms yet again that perception trumps reality. The FastPass concept is an absolute stroke of genius; it utterly changes perceived waiting times and has made many, many park-goers very, very giddy.<br />
</blockquote><br />
<br />
3. An oft-repeated and perhaps apocryphal operations research/statistics/decision theory anecdote has to do with elevators in a very large office building. Employees complained about excessive waiting times because the elevators all too frequently seemed to be in lockstep. Any physical solution such as creating a new elevator shaft or installing a complicated timing algorithm would be very expensive. The famous and utterly inexpensive psychological solution whereby perception trumped reality was to put in mirrors so that the waiting time would seem less because the employees would enjoy admiring themselves in the mirrors. Note that older and more benighted operations research/statistics/decision theory textbooks would have used the word “women” instead of “employees” in the previous sentence. <br />
<br />
4. A very modern and frustrating example of perception again trumping reality can often be observed in supermarkets which have installed self-checkout lanes without placing a limit on the number of items per shopper. In order to avoid a line at the regular checkout, some shoppers with an extremely large number of items will often choose the self-checkout and take much longer to finish than if had they queued at the regular checkout. Explain why said shoppers psychologically might prefer to persist in that behavior despite evidence to the contrary. Why don’t supermarkets simply limit the number of items per customer at self-checkout lanes?<br />
<br />
Submitted by Paul Alper<br />
<br />
==Don’t forget Chebyshev==<br />
<i>Super Crunchers</i>, by Ian Ayres, Random House, 2007<br><br />
<br />
<blockquote>When I taught at Stanford Law School, professors were required to award grades that had a 3.2 mean. …. The problem was that many of the students and many of the professors had no way to express the degree of variability in professors’ grading habits. …. As a nation, we lack a vocabulary of dispersion. We don’t know how to express what we intuitively know about the variability of a distribution of numbers. The 2SD [2 standard-deviation] rule could help give us this vocabulary. A professor who said that her standard deviation was .2 could have conveyed a lot of information with a single number. The problem is that very few people in the U.S. today understand what this means. But you should know and be able to explain to others that only about 2.5 percent of the professor’s grades are above 3.6. [pp. 221-222]</blockquote><br />
<br />
===Discussion===<br />
1. Suppose that a professor's <i>awarded</i> grades had mean 3.2 and SD 0.2.<br> <br />
(a) Under what condition could we say that “only about 2.5 percent of the professor’s grades are above 3.6”?<br><br />
(b) Without that condition, what could we say, if anything, about the percent of awarded grades <i>outside</i> of a 2SD range about the mean? About the percent of awarded grades <i>above</i> 3.6?<br><br />
2. Suppose that a professor's <i>raw grades</i> had mean 3.2 and SD 0.2. Do you think that this would be a realistic scenario in most undergraduate college classes? In most graduate-school classes? Why or why not?<br><br />
3. How could a professor construct a distribution of <i>awarded</i> grades with mean 3.2 and SD 0.2, based on <i>raw grades</i>, so that one could say that only about 2.5 percent of the <i>awarded</i> grades are above 3.6? What effect, if any, could that scaling have had on the worst – or on the best – raw grades?<br><br />
<br />
Submitted by Margaret Cibes<br />
<br />
==Critique of women-in-science statistics==<br />
[http://scholarship.claremont.edu/cgi/viewcontent.cgi?article=1041&context=jhm&sei-redir=1&referer=http%3A%2F%2Fwww.google.com%2Furl%3Fsa%3Dt%26rct%3Dj%26q%3Drumors%2520of%2520rarity%26source%3Dweb%26cd%3D1%26ved%3D0CCMQFjAA%26url%3Dhttp%253A%252F%252Fscholarship.claremont.edu%252Fcgi%252Fviewcontent.cgi%253Farticle%253D1041%2526context%253Djhm%26ei%3DugheT7arHMPV0QGzpfjkAw%26usg%3DAFQjCNGPJtXVTh10gldzpdqxaolp1Shd8Q#search=%22rumors%20rarity%22 “Rumors of Our Rarity are Greatly Exaggerated: Bad Statistics About Women in Science”]<br><br />
by Cathy Kessel, <i>Journal of Humanistic Mathematics</i>, July 2011<br><br />
<br />
Based on her apparently extensive and detailed study of reports about female-to-male ratios with respect to STEM abilities/careers, Kessel discusses three major problems with the statistics cited in them, as well as with the repetition of these questionable figures in subsequent academic and non-academic reports. <br />
<blockquote>Whatever their origins, statistics which are mislabeled, misinterpreted, fictitious, or otherwise defective remain in circulation because they are accepted by editors, readers, and referees.</blockquote><br />
<br />
“The Solitary Statistic.” A 13-to-1 boy-girl ratio in SAT-Math scores has been widely cited since it appeared in a 1983 <i>Science</i> article. That ratio was based on the scores of 280 seventh- and eighth-graders who scored 700 or above on the test over the period 1980-83. These students were part of a total of 64,000 students applying for a Johns Hopkins science program for exceptionally talented STEM-potential students. Kessel faults the widespread references to this outdated data, among other issues, and she cites more recent statistics at Hopkins and other such programs, including a ratio as low as 3 to 1 in 2005.<br><br />
<br />
“The Fabricated Statistic.” A “finding” that “Women talk almost three times as much as men” was published in <i>The Female Brain</i> in 2006. This was supposed to explain why women prefer careers which allow them to “connect and communicate” as opposed careers in science and engineering. Kessel outlines some issues that might make this explanation questionable.<br><br />
<br />
“The Garbled Statistic.” An example from “The Science of Sex Differences in Science and Mathematics,” published in <i>Psychological Science in the Public Interest</i> in 2007, was a report that women were “8.3% of tenure-track faculty at ‘elite’ mathematics departments.” A 2002 survey produced similar math data; that survey was based on the “top 50 departments.” These and other reports generally reported only the aggregate figure and not any of the raw data by rank. Kessel gives other examples in which raw data summary tables (which she had requested and received) would have been helpful to interpreting results.<br />
<blockquote>Although noticing mistakes may require numerical sophistication or knowledge of particular fields, accurate reporting of names, dates, and sources of<br />
statistics does not take much skill. At the very least, authors and research assistants can copy categories and sources as well as numbers. Editors can (and should) ask for sources.</blockquote><br />
===Discussion===<br />
1. Is there anything random about the group of students applying to a university’s program for talented students - or about the top SAT-M scorers in that group? Why are these important questions?<br><br />
2. Kessel quotes a statement that has been reported a number of times: “Women use 20,000 words per day, while men use 7,000." How do you think the researchers got these counts?<br><br />
3. Why might it be important to consider academic rank as a variable in analyzing the progress, or lack thereof, of women in obtaining university positions?<br><br />
4. Why might it be important to know more about the sponsorship of these studies – researcher affiliations, funding, <i>etc.</i>?<br><br />
<br />
Submitted by Margaret Cibes, based on a reference in March 2012 <i>College Mathematics Journal</i><br />
<br />
==Ethics study of social classes==<br />
[http://blogs.wsj.com/ideas-market/2012/02/27/study-high-social-class-predicts-unethical-behavior/tab/print/ “Study: High Social Class Predicts Unethical Behavior”]<br><br />
<i>The Wall Street Journal</i>, February 27, 2012<br><br />
<br />
Here is an abstract of the study[http://www.pnas.org/content/early/2012/02/21/1118373109.abstract] referred to in the article:<br />
<blockquote>Seven studies using experimental and naturalistic methods reveal that upper-class individuals behave more unethically than lower-class individuals. In studies 1 and 2, upper-class individuals were more likely to break the law while driving, relative to lower-class individuals. In follow-up laboratory studies, upper-class individuals were more likely to exhibit unethical decision-making tendencies (study 3), take valued goods from others (study 4), lie in a negotiation (study 5), cheat to increase their chances of winning a prize (study 6), and endorse unethical behavior at work (study 7) than were lower-class individuals. Mediator and moderator data demonstrated that upper-class individuals’ unethical tendencies are accounted for, in part, by their more favorable attitudes toward greed.</blockquote><br />
See also [http://www.pnas.org/content/suppl/2012/02/22/1118373109.DCSupplemental/pnas.201118373SI.pdf "Supporting Information"], published online in Proceedings of the National Academy of Sciences of the USA, February 27, 2012.<br><br />
<br />
===Discussion===<br />
1. If you were going to write an article about this study, and you had access to the entire report, what would be the first, most basic, information you would want to provide to your readers about the “class” categories referred to in the abstract?<br><br />
2. The article indicates that the sample sizes for the first three experiments were “250,” “150 drivers,” and “105 students.” Besides the relatively small sample sizes, what other issues can you identify as a potential problems in making any inference about ethics from these experimental results? <br />
<br />
Submitted by Margaret Cibes<br />
<br />
==Judea Pearl wins Turing Prize==<br />
Danny Kaplan posted a link to this story on the Isolated Statisicians e-mail list:<br />
<br />
[http://bits.blogs.nytimes.com/2012/03/15/a-turing-award-for-helping-make-computers-smarter/?hpw A Turing Award for helping make computers smarter].<br><br />
by Steve Lohr, Bits blog, ''New York Times'', 15 March 2012<br />
<br />
Judea Pearl of UCLA has been awarded this year's Turing Prize by the Association for Computing Machinery. According to the article Pearl's work on probabilistic reasoning and Bayesian networks has influenced applications in areas from search engines to fraud detection to speech recognition. The article includes testimonials from many noted experts in the field of artificial intelligence.<br />
<br />
Danny's message provided links to [http://bayes.cs.ucla.edu/jp_home.html Pearl's web page] for references to his work on causality, and to this [http://singapore.cs.ucla.edu/LECTURE/lecture_sec1.htm talk], which is the epilogue to his famous book, [http://bayes.cs.ucla.edu/BOOK-99/book-toc.html ''Causality''].<br />
<br />
Danny's [http://www.mosaic-web.org/go/StatisticalModeling ''Statistical Modeling'' textbook] includes a [http://www.mosaic-web.org/go/StatisticalModeling/Chapters/Chapter-17.pdf chapter] which discusses some of these ideas at a level appropriate for an introductory statistics audience.<br />
<br />
==A bizarre anatomical correlation==<br />
<br />
[http://www.nytimes.com/2012/03/17/opinion/collins-politicians-swinging-stethoscopes.html Politicians Swinging Stethoscopes], Gail Collins, The New York Times, March 16, 2012.<br />
<br />
When a topic carries strong emotions, often people forget to check their facts carefully. And abortion is possibly the most emotional topic in politics today. It's not too surprising that opponents of abortion have tried to promote a link between abortion and breast cancer.<br />
<br />
<blockquote>New Hampshire, for instance, seems to have developed a thing for linking sex and malignant disease. This week, the State House passed a bill that required that women who want to terminate a pregnancy be informed that abortions were linked to "an increased risk of breast cancer." As Terie Norelli, the minority leader, put it, the Legislature is attempting to make it a felony for a doctor "to not give a patient inaccurate information."</blockquote><br />
<br />
This was actually an issue [http://en.wikipedia.org/wiki/C._Everett_Koop#Government_service about 25 years ago, when C. Everett Koop was Surgeon General]. The American Cancer Society (ACS) has written that [http://www.cancer.org/Cancer/BreastCancer/MoreInformation/is-abortion-linked-to-breast-cancer scientific research studies have not found a cause-and-effect relationship between abortion and breast cancer], and cites a comprehensive review in 2003 by the National Cancer Institute (NCI). But numerous pro-life sites still claim the opposite, with headlines like [http://www.lifenews.com/2010/09/21/nat-6718/ Hundreds of Studies Confirm Abortion-Breast Cancer Link].<br />
<br />
It is interesting to speculate why pro-life sites would promote the abortion/breast cancer link so strongly in spite of dismissive commentary from respected organizations like ACS and NCI. If you believe that abortion is murder (as many people do), then it is not too far a leap to believe that something this evil would necessarily carry bad health consequences at the same time. It may be a belief that mainstream organizations like ACS and NCI are dominated by [http://www.wnd.com/2010/01/121749/ pro-abortion extremists].<br />
<br />
It is still curious, because if abortion is immoral, it should not matter whether it has bad side effects, such as an increased risk of breast cancer, or good side effects, [http://en.wikipedia.org/wiki/The_Impact_of_Legalized_Abortion_on_Crime such as a decrease in the crime rate]. It may be that opponents of abortion feel free to use any weapon at their disposal to dissuade women from choosing abortion. It is worth noting that opponents of abortion are not the first and will not be the last group that has seized on a dubious statistical finding to support their political perspective.<br />
<br />
The abortion/breast cancer link at least has biological plausibility. The number of pregnancies that you have and the number of live births are indeed associated with various types of cancer, so it is not too far fetched to believe that abortion might be related to these cancers as well. But another cancer link in an area almost as contentious lacks even this biological plausibility.<br />
<br />
<blockquote>And there’s more. One of the sponsors, Representative Jeanine Notter, recently asked a colleague whether he would be interested, "as a man," to know that there was a study "that links the pill to prostate cancer."</blockquote><br />
<br />
Clearly, Ms. Notter understands that only women consume birth control pills and that only men have a prostate. What she is claiming is <br />
<br />
<blockquote>that nations with high use of birth control pills among women also tended to have high rates of prostate cancer among men.</blockquote><br />
<br />
Gail Collins mocks this correlation.<br />
<br />
<blockquote>You could also possibly discover that nations with the lowest per capita number of ferrets have a higher rate of prostate cancer.</blockquote><br />
<br />
Birth control pills are possibly associated with ovarian and cervical cancer, and these are two organs that women do have. They may also be associated with a decrease in the risk of endometrial cancer. If you don't believe that NCI is overrun by extremists, you might find [http://www.cancer.gov/cancertopics/factsheet/Risk/oral-contraceptives this fact sheet] to offer a helpful review of these risks. Disentangling these cancer risks from other confounders (such as the age at first sexual intercourse) is very difficult.<br />
<br />
===Questions===<br />
<br />
1. What is the name for the type of study that notes that "nations with high use of birth control pills among women also tended to have high rates of prostate cancer among men"?<br />
<br />
2. Randomized studies of the link between abortion and breast cancer are clearly impossible. What types of observational studies might be used to examine this link? What are the strengths and weaknesses of those types of studies.</div>Simon66217https://www.causeweb.org/wiki/chance/index.php?title=Chance_News_83&diff=15451Chance News 832012-03-19T16:39:32Z<p>Simon66217: /* A bizarre anatomical correlation */</p>
<hr />
<div>==Quotations==<br />
“A poll is not laser surgery; it’s an estimate.”<br />
<div align=right>ABC News polling director in [http://abcnews.go.com/blogs/politics/2007/12/moe-and-mojo/ “MOE and Mojo”]<br><br />
ABC Blogs, December 3, 2007</div><br />
Submitted by Margaret Cibes<br />
<br />
----<br />
"The most famous result of Student’s experimental method is Student’s t-table. But the real end of Student’s inquiry was taste, quality control, and minimally efficient sample sizes for experimental Guinness – not to achieve statistical significance at the .05 level or, worse yet, boast about an artificially randomized experiment."<br />
<br />
<div align=right>--Stephen T. Ziliak, in [http://sites.roosevelt.edu/sziliak/files/2012/02/William-S-Gosset-and-Experimental-Statistics-Ziliak-JWE-2011.pdf W.S. Gosset and some neglected concepts in experimental statistics: Guinnessometrics II]</div><br />
<br />
(Ziliak is the co-author of [http://www.press.umich.edu/titleDetailPraise.do?id=186351 ''The Cult of Statistical Significance: How the Standard Error Costs Us Jobs, Justice, and Lives''])<br />
<br />
Submitted by Bill Peterson<br />
<br />
-----<br />
“[W. S. Gossett] wrote to R. A. Fisher of the t tables, "You are probably the only man who will ever use them (Box 1978)."<br><br />
<br />
“[W]e see the data analyst's insistence on ‘letting the data speak to us’ by plots and displays as an instinctive understanding of the need to encourage and to stimulate the pattern recognition and model generating capability of the right brain. Also, it expresses his concern that we not allow our pushy deductive left brain to take over too quickly and perhaps forcibly produce unwarranted conclusions based on an inadequate model.”<br />
<div align=right>George Box in “The Importance of Practice in the Development of Statistics”<br><br />
<i>Technometrics</i>, February 1984</div><br />
<br />
Thomas L. Moore recommended this article in an ISOSTAT posting. (It is available in [http://www.jstor.org/discover/10.2307/1268410?uid=3739952&uid=2129&uid=2134&uid=2&uid=70&uid=4&uid=3739256&sid=21100643590836 JSTOR].)<br />
<br />
----<br />
We are familiar with George Box’s famous statement: “All models are wrong but some are useful.” Here is another variant, cited in [http://en.wikiquote.org/wiki/George_E._P._Box Wikipedia]:<br />
:“Remember that all models are wrong; the practical question is how wrong do they have to be to not be useful.”<br />
<br />
Submitted by Margaret Cibes<br />
<br />
(Note. For an interesting discussion revisiting this theme, see [http://andrewgelman.com/2012/03/all-models-are-right-most-are-useless/ All models are right, most are useless] on Andrew Gelman's blog, 4 March 2012).<br />
----<br />
“There are two kinds of statistics: the kind you look up and the kind you make up.”<br />
<br />
<div align=right> --attributed to Rex Stout, American writer (1886 - 1975)</div><br />
Submitted by Paul Alper<br />
<br />
<br />
----<br />
<br />
“Definition of Statistics: The science of producing unreliable facts from reliable figures.”<br />
<br />
"The only science that enables different experts using the same figures to draw different conclusions."<br />
<br />
<div align=right> --attributed to Evan Esar, American humorist (1899–1995)</div><br />
Submitted by Paul Alper<br />
<br />
----<br />
“Science involves confronting our `absolute stupidity'. That kind of stupidity is an existential fact, inherent in our efforts to push our way into the unknown. …. Focusing on important questions puts us in the awkward position of being ignorant. One of the beautiful things about science is that it allows us to bumble along, getting it wrong time after time, and feel perfectly fine as long as we learn something each time. …. The more comfortable we become with being stupid, the deeper we will wade into the unknown and the more likely we are to make big discoveries.”<br />
<div align=right>UVa scientist Martin Schwartz in [http://jcs.biologists.org/content/121/11/1771.full “The importance of stupidity in scientific research”]<br><br />
<i>Journal of Cell Science</i>, 2008</div><br />
Submitted by Margaret Cibes<br />
<br />
==Forsooth==<br />
“In the first four months [at the new Resorts World Casino New York City], roughly 25,000 gamblers showed up every day, shoving a collective $2.3 billion through the slots and losing $140 million in the process. …. Resorts World offers more than 4,000 slot machines, but thanks to state law, there are no traditional card tables.”<br />
<div align=right>[http://online.wsj.com/article/SB10001424052970204792404577229481078006746.html?link=SM_clmst_sum#articleTabs%3Dcomments “The Gamblers’ New Game”]<br><br />
<i>The Wall Street Journal</i>, February 18, 2012</div><br />
Submitted by Margaret Cibes<br />
<br />
-----<br />
[[File:Elderly_drivers.jpg|400px]]<br><br><br />
“Drivers 85 and older still have a higher rate of deadly crashes than any other age group except teenagers.”<br><br />
<br />
(The article also describes two women who have learned to "compensate" for their macular degeneration in various ways - not necessarily welcome news!)<br />
<div align=right>[http://online.wsj.com/article/SB10001424052970204520204577251310179633818.html?KEYWORDS=sue+shellenbarger “Safer Over 70: Divers Keep the Keys”]<br><br />
<i>The Wall Street Journal</i>, February 29, 2012</div><br />
Submitted by Margaret Cibes<br />
<br />
-----<br />
[[File:PieChartMost.png|300px]]<br><br><br />
See the "Observation" at the top of the chart.<br />
<div align=right><br />
[http://junkcharts.typepad.com/junk_charts/2012/02/the-meaning-of-most.html “The Meaning of most”], downloaded from Junk Charts, March 1, 2012<br><br />
originally cited in [http://blog.kissmetrics.com/loading-time/?wide=1 “Mobile vs. Desktop”], KISSmetrics</div><br />
Submitted by Margaret Cibes<br />
<br />
-----<br />
<br />
“Here is the rub: Apple is so big, it’s running up against the law of large numbers. Also known as the golden theorem, with a proof attributed to the 17th-century Swiss mathematician Jacob Bernoulli, the law states that a variable will revert to a mean over a large sample of results. In the case of the largest companies, it suggests that high earnings growth and a rapid rise in share price will slow as those companies grow ever larger.”<br />
<div align=right>James Stewart in [http://www.nytimes.com/2012/02/25/business/apple-confronts-the-law-of-large-numbers-common-sense.html?_r=2&hp “Confronting a Law of Limits”]<br><br />
<i>The New York Times</i>, February 24, 2012</div><br />
Bill Peterson found Andrew Gelman’s comments[http://andrewgelman.com/2012/02/apple-confronts-the-law-of-large-numbers-huh/] about this article. <br />
<br />
Submitted by Margaret Cibes<br />
<br />
==Kaiser Fung on Minnesota’s ramp meters==<br />
<br />
A number references to Kaiser Fung’s book, ''Numbers Rule Your World'', appear in [http://test.causeweb.org/wiki/chance/index.php/Chance_News_82 Chance News 82]. From a Minnesotan’s point of view, however, the most important topic he discusses is not hurricanes, not drug testing, and not bias in standardized testing. Rather, the most critical issue is ramp metering as a means of improving traffic flow, relieving congestion and reducing travel time on Minnesota highways. “Industry experts regard Minnesota’s system of 430 ramp meters as a national model.”<br />
<br />
Unfortunately, “perception trumped reality.” An influential state senator, Dick Day, now a lobbyist for gambling interests, “led a charge to abolish the nationally recognized program, portraying it as part of the problem, not the solution.”<br />
<blockquote><br />
Leave it to Senator Day to speak the minds of “average Joes”--the people he meets at coffee shops, county fairs, summer parades, and the stock car races he loves. He saw ramp metering as a symbol of Big Government strangling our liberty.<br><br><br />
In the Twin Cities, drivers perceived their trip times to have lengthened [due to the ramp meters] even though in reality they have probably decreased. Thus, when in September 2000, the state legislature passed a mandate requiring MnDOT [Minnesota Department of Transportation] to conduct a “meters shutoff” experiment [of six weeks], the engineers [who devised the metering program] were stunned and disillusioned.<br />
</blockquote><br />
<br />
To make a long story short, when the ramp meters came back on, it turns out that: <br />
<blockquote><br />
[T]he engineering vision triumphed. Freeway conditions indeed worsened after the ramp meters went off. The key findings, based on actual measurements were as follows:<br />
*Peak freeway volume dropped by 9 percent.<br />
*Travel times rose by 22 percent, and the reliability deteriorated.<br />
*Travel speeds declined by 7 percent.<br />
*The number of crashes during merges jumped by 26 percent.<br />
</blockquote><br />
<br />
“The consultants further estimated that the benefits of ramp metering outweighed costs by five to one.” Nevertheless, the-above objective measures had to continue to battle subjective ones:<br />
<blockquote><br />
Despite the reality that commuters shortened their journeys if they waited their turns at the ramps, the drivers did not perceive the trade-off to be beneficial; they insisted that they would rather be moving slowly on the freeway than coming to a standstill at the ramp.<br />
</blockquote><br />
<br />
Accordingly, the engineers decided to modify the optimum solution to take into account driver psychology. “When they turned the lights back on, they limited waiting time on the ramps to four minutes, retired some unnecessary meters, and also shortened the operating hours.” Said differently, the constrained optimization model the engineers first considered left out some pivotal constraints.<br />
<br />
===Discussion===<br />
<br />
1. Do a search for “behavioral economics” to see the prevalence of irrational perceptions and subjective calculations in the economic sphere.<br />
<br />
2. Fung discusses an allied, albeit inverse, problem of waiting-time misconception. This instance concerns Disney World and its popular so-called FastPass as a means of avoiding queues. According to Fung<br />
<blockquote><br />
Clearly, FastPass users love the product--but how much waiting time can they save? Amazingly, the answer is none; they spend the same amount of time waiting for popular rides with or without FastPass!..So Disney confirms yet again that perception trumps reality. The FastPass concept is an absolute stroke of genius; it utterly changes perceived waiting times and has made many, many park-goers very, very giddy.<br />
</blockquote><br />
<br />
3. An oft-repeated and perhaps apocryphal operations research/statistics/decision theory anecdote has to do with elevators in a very large office building. Employees complained about excessive waiting times because the elevators all too frequently seemed to be in lockstep. Any physical solution such as creating a new elevator shaft or installing a complicated timing algorithm would be very expensive. The famous and utterly inexpensive psychological solution whereby perception trumped reality was to put in mirrors so that the waiting time would seem less because the employees would enjoy admiring themselves in the mirrors. Note that older and more benighted operations research/statistics/decision theory textbooks would have used the word “women” instead of “employees” in the previous sentence. <br />
<br />
4. A very modern and frustrating example of perception again trumping reality can often be observed in supermarkets which have installed self-checkout lanes without placing a limit on the number of items per shopper. In order to avoid a line at the regular checkout, some shoppers with an extremely large number of items will often choose the self-checkout and take much longer to finish than if had they queued at the regular checkout. Explain why said shoppers psychologically might prefer to persist in that behavior despite evidence to the contrary. Why don’t supermarkets simply limit the number of items per customer at self-checkout lanes?<br />
<br />
Submitted by Paul Alper<br />
<br />
==Don’t forget Chebyshev==<br />
<i>Super Crunchers</i>, by Ian Ayres, Random House, 2007<br><br />
<br />
<blockquote>When I taught at Stanford Law School, professors were required to award grades that had a 3.2 mean. …. The problem was that many of the students and many of the professors had no way to express the degree of variability in professors’ grading habits. …. As a nation, we lack a vocabulary of dispersion. We don’t know how to express what we intuitively know about the variability of a distribution of numbers. The 2SD [2 standard-deviation] rule could help give us this vocabulary. A professor who said that her standard deviation was .2 could have conveyed a lot of information with a single number. The problem is that very few people in the U.S. today understand what this means. But you should know and be able to explain to others that only about 2.5 percent of the professor’s grades are above 3.6. [pp. 221-222]</blockquote><br />
<br />
===Discussion===<br />
1. Suppose that a professor's <i>awarded</i> grades had mean 3.2 and SD 0.2.<br> <br />
(a) Under what condition could we say that “only about 2.5 percent of the professor’s grades are above 3.6”?<br><br />
(b) Without that condition, what could we say, if anything, about the percent of awarded grades <i>outside</i> of a 2SD range about the mean? About the percent of awarded grades <i>above</i> 3.6?<br><br />
2. Suppose that a professor's <i>raw grades</i> had mean 3.2 and SD 0.2. Do you think that this would be a realistic scenario in most undergraduate college classes? In most graduate-school classes? Why or why not?<br><br />
3. How could a professor construct a distribution of <i>awarded</i> grades with mean 3.2 and SD 0.2, based on <i>raw grades</i>, so that one could say that only about 2.5 percent of the <i>awarded</i> grades are above 3.6? What effect, if any, could that scaling have had on the worst – or on the best – raw grades?<br><br />
<br />
Submitted by Margaret Cibes<br />
<br />
==Critique of women-in-science statistics==<br />
[http://scholarship.claremont.edu/cgi/viewcontent.cgi?article=1041&context=jhm&sei-redir=1&referer=http%3A%2F%2Fwww.google.com%2Furl%3Fsa%3Dt%26rct%3Dj%26q%3Drumors%2520of%2520rarity%26source%3Dweb%26cd%3D1%26ved%3D0CCMQFjAA%26url%3Dhttp%253A%252F%252Fscholarship.claremont.edu%252Fcgi%252Fviewcontent.cgi%253Farticle%253D1041%2526context%253Djhm%26ei%3DugheT7arHMPV0QGzpfjkAw%26usg%3DAFQjCNGPJtXVTh10gldzpdqxaolp1Shd8Q#search=%22rumors%20rarity%22 “Rumors of Our Rarity are Greatly Exaggerated: Bad Statistics About Women in Science”]<br><br />
by Cathy Kessel, <i>Journal of Humanistic Mathematics</i>, July 2011<br><br />
<br />
Based on her apparently extensive and detailed study of reports about female-to-male ratios with respect to STEM abilities/careers, Kessel discusses three major problems with the statistics cited in them, as well as with the repetition of these questionable figures in subsequent academic and non-academic reports. <br />
<blockquote>Whatever their origins, statistics which are mislabeled, misinterpreted, fictitious, or otherwise defective remain in circulation because they are accepted by editors, readers, and referees.</blockquote><br />
<br />
“The Solitary Statistic.” A 13-to-1 boy-girl ratio in SAT-Math scores has been widely cited since it appeared in a 1983 <i>Science</i> article. That ratio was based on the scores of 280 seventh- and eighth-graders who scored 700 or above on the test over the period 1980-83. These students were part of a total of 64,000 students applying for a Johns Hopkins science program for exceptionally talented STEM-potential students. Kessel faults the widespread references to this outdated data, among other issues, and she cites more recent statistics at Hopkins and other such programs, including a ratio as low as 3 to 1 in 2005.<br><br />
<br />
“The Fabricated Statistic.” A “finding” that “Women talk almost three times as much as men” was published in <i>The Female Brain</i> in 2006. This was supposed to explain why women prefer careers which allow them to “connect and communicate” as opposed careers in science and engineering. Kessel outlines some issues that might make this explanation questionable.<br><br />
<br />
“The Garbled Statistic.” An example from “The Science of Sex Differences in Science and Mathematics,” published in <i>Psychological Science in the Public Interest</i> in 2007, was a report that women were “8.3% of tenure-track faculty at ‘elite’ mathematics departments.” A 2002 survey produced similar math data; that survey was based on the “top 50 departments.” These and other reports generally reported only the aggregate figure and not any of the raw data by rank. Kessel gives other examples in which raw data summary tables (which she had requested and received) would have been helpful to interpreting results.<br />
<blockquote>Although noticing mistakes may require numerical sophistication or knowledge of particular fields, accurate reporting of names, dates, and sources of<br />
statistics does not take much skill. At the very least, authors and research assistants can copy categories and sources as well as numbers. Editors can (and should) ask for sources.</blockquote><br />
===Discussion===<br />
1. Is there anything random about the group of students applying to a university’s program for talented students - or about the top SAT-M scorers in that group? Why are these important questions?<br><br />
2. Kessel quotes a statement that has been reported a number of times: “Women use 20,000 words per day, while men use 7,000." How do you think the researchers got these counts?<br><br />
3. Why might it be important to consider academic rank as a variable in analyzing the progress, or lack thereof, of women in obtaining university positions?<br><br />
4. Why might it be important to know more about the sponsorship of these studies – researcher affiliations, funding, <i>etc.</i>?<br><br />
<br />
Submitted by Margaret Cibes, based on a reference in March 2012 <i>College Mathematics Journal</i><br />
<br />
==Ethics study of social classes==<br />
[http://blogs.wsj.com/ideas-market/2012/02/27/study-high-social-class-predicts-unethical-behavior/tab/print/ “Study: High Social Class Predicts Unethical Behavior”]<br><br />
<i>The Wall Street Journal</i>, February 27, 2012<br><br />
<br />
Here is an abstract of the study[http://www.pnas.org/content/early/2012/02/21/1118373109.abstract] referred to in the article:<br />
<blockquote>Seven studies using experimental and naturalistic methods reveal that upper-class individuals behave more unethically than lower-class individuals. In studies 1 and 2, upper-class individuals were more likely to break the law while driving, relative to lower-class individuals. In follow-up laboratory studies, upper-class individuals were more likely to exhibit unethical decision-making tendencies (study 3), take valued goods from others (study 4), lie in a negotiation (study 5), cheat to increase their chances of winning a prize (study 6), and endorse unethical behavior at work (study 7) than were lower-class individuals. Mediator and moderator data demonstrated that upper-class individuals’ unethical tendencies are accounted for, in part, by their more favorable attitudes toward greed.</blockquote><br />
See also [http://www.pnas.org/content/suppl/2012/02/22/1118373109.DCSupplemental/pnas.201118373SI.pdf "Supporting Information"], published online in Proceedings of the National Academy of Sciences of the USA, February 27, 2012.<br><br />
<br />
===Discussion===<br />
1. If you were going to write an article about this study, and you had access to the entire report, what would be the first, most basic, information you would want to provide to your readers about the “class” categories referred to in the abstract?<br><br />
2. The article indicates that the sample sizes for the first three experiments were “250,” “150 drivers,” and “105 students.” Besides the relatively small sample sizes, what other issues can you identify as a potential problems in making any inference about ethics from these experimental results? <br />
<br />
Submitted by Margaret Cibes<br />
<br />
==Judea Pearl wins Turing Prize==<br />
Danny Kaplan posted a link to this story on the Isolated Statisicians e-mail list:<br />
<br />
[http://bits.blogs.nytimes.com/2012/03/15/a-turing-award-for-helping-make-computers-smarter/?hpw A Turing Award for helping make computers smarter].<br><br />
by Steve Lohr, Bits blog, ''New York Times'', 15 March 2012<br />
<br />
Judea Pearl of UCLA has been awarded this year's Turing Prize by the Association for Computing Machinery. According to the article Pearl's work on probabilistic reasoning and Bayesian networks has influenced applications in areas from search engines to fraud detection to speech recognition. The article includes testimonials from many noted experts in the field of artificial intelligence.<br />
<br />
Danny's message provided links to [http://bayes.cs.ucla.edu/jp_home.html Pearl's web page] for references to his work on causality, and to this [http://singapore.cs.ucla.edu/LECTURE/lecture_sec1.htm talk], which is the epilogue to his famous book, [http://bayes.cs.ucla.edu/BOOK-99/book-toc.html ''Causality''].<br />
<br />
Danny's [http://www.mosaic-web.org/go/StatisticalModeling ''Statistical Modeling'' textbook] includes a [http://www.mosaic-web.org/go/StatisticalModeling/Chapters/Chapter-17.pdf chapter] which discusses some of these ideas at a level appropriate for an introductory statistics audience.<br />
<br />
==A bizarre anatomical correlation==<br />
<br />
[http://www.nytimes.com/2012/03/17/opinion/collins-politicians-swinging-stethoscopes.html Politicians Swinging Stethoscopes], Gail Collins, The New York Times, March 16, 2012.<br />
<br />
When a topic carries strong emotions, often people forget to check their facts carefully. And abortion is possibly the most emotional topic in politics today. It's not too surprising that opponents of abortion have tried to promote a link between abortion and breast cancer.<br />
<br />
<blockquote>New Hampshire, for instance, seems to have developed a thing for linking sex and malignant disease. This week, the State House passed a bill that required that women who want to terminate a pregnancy be informed that abortions were linked to "an increased risk of breast cancer." As Terie Norelli, the minority leader, put it, the Legislature is attempting to make it a felony for a doctor "to not give a patient inaccurate information."</blockquote><br />
<br />
This was actually an issue [http://en.wikipedia.org/wiki/C._Everett_Koop#Government_service about 25 years ago, when C. Everett Koop was Surgeon General]. The American Cancer Society (ACS) has written that [http://www.cancer.org/Cancer/BreastCancer/MoreInformation/is-abortion-linked-to-breast-cancer scientific research studies have not found a cause-and-effect relationship between abortion and breast cancer], and cites a comprehensive review in 2003 by the National Cancer Institute (NCI). But numerous pro-life sites still claim the opposite, with headlines like [http://www.lifenews.com/2010/09/21/nat-6718/ Hundreds of Studies Confirm Abortion-Breast Cancer Link].<br />
<br />
It is interesting to speculate why pro-life sites would promote the abortion/breast cancer link so strongly in spite of dismissive commentary from respected organizations like ACS and NCI. If you believe that abortion is murder (as many people do), then it is not too far a leap to believe that something this evil would necessarily carry bad health consequences at the same time. It may be a belief that mainstream organizations like ACS and NCI are dominated by [http://www.wnd.com/2010/01/121749/ pro-abortion extremists].<br />
<br />
It is still curious, because if abortion is immoral, it should not matter whether it has bad side effects, such as an increased risk of breast cancer, or good side effects, [http://en.wikipedia.org/wiki/The_Impact_of_Legalized_Abortion_on_Crime such as a decrease in the crime rate]. It may be that opponents of abortion feel free to use any weapon at their disposal to dissuade women from choosing abortion. It is worth noting that opponents of abortion are not the first and will not be the last group that has seized on a dubious statistical finding to support their political perspective.<br />
<br />
The abortion/breast cancer link at least has biological plausibility. The number of pregnancies that you have and the number of live births are indeed associated with various types of cancer, so it is not too far fetched to believe that abortion might be related to these cancers as well. But another cancer link in an area almost as contentious lacks even this biological plausibility.<br />
<br />
<blockquote>And there’s more. One of the sponsors, Representative Jeanine Notter, recently asked a colleague whether he would be interested, "as a man," to know that there was a study "that links the pill to prostate cancer."</blockquote><br />
<br />
Clearly, Ms. Notter understands that only women consume birth control pills and that only men have a prostate. What she is claiming is <br />
<br />
<blockquote>that nations with high use of birth control pills among women also tended to have high rates of prostate cancer among men.</blockquote><br />
<br />
Gail Collins mocks this correlation.<br />
<br />
<blockquote>You could also possibly discover that nations with the lowest per capita number of ferrets have a higher rate of prostate cancer.</blockquote><br />
<br />
Birth control pills are possibly associated with ovarian and cervical cancer, and these are two organs that women do have. They may also be associated with a decrease in the risk of endometrial cancer. If you don't believe that NCI is overrun by extremists, you might find [http://www.cancer.gov/cancertopics/factsheet/Risk/oral-contraceptives this fact sheet] to offer a helpful review of these risks. Disentangling these cancer risks from other confounders (such as the age at first sexual intercourse) is very difficult.<br />
<br />
===Questions===<br />
<br />
1. What is the name for the type of study that notes that "nations with high use of birth control pills among women also tended to have high rates of prostate cancer among men"?<br />
<br />
2. Randomized studies of the link between abortion and breast cancer are clearly impossible. What types of observational studies might be used to examine this link. What are the strengths and weaknesses of those types of studies.</div>Simon66217https://www.causeweb.org/wiki/chance/index.php?title=Chance_News_83&diff=15450Chance News 832012-03-19T16:34:33Z<p>Simon66217: /* A bizarre anatomical correlation */</p>
<hr />
<div>==Quotations==<br />
“A poll is not laser surgery; it’s an estimate.”<br />
<div align=right>ABC News polling director in [http://abcnews.go.com/blogs/politics/2007/12/moe-and-mojo/ “MOE and Mojo”]<br><br />
ABC Blogs, December 3, 2007</div><br />
Submitted by Margaret Cibes<br />
<br />
----<br />
"The most famous result of Student’s experimental method is Student’s t-table. But the real end of Student’s inquiry was taste, quality control, and minimally efficient sample sizes for experimental Guinness – not to achieve statistical significance at the .05 level or, worse yet, boast about an artificially randomized experiment."<br />
<br />
<div align=right>--Stephen T. Ziliak, in [http://sites.roosevelt.edu/sziliak/files/2012/02/William-S-Gosset-and-Experimental-Statistics-Ziliak-JWE-2011.pdf W.S. Gosset and some neglected concepts in experimental statistics: Guinnessometrics II]</div><br />
<br />
(Ziliak is the co-author of [http://www.press.umich.edu/titleDetailPraise.do?id=186351 ''The Cult of Statistical Significance: How the Standard Error Costs Us Jobs, Justice, and Lives''])<br />
<br />
Submitted by Bill Peterson<br />
<br />
-----<br />
“[W. S. Gossett] wrote to R. A. Fisher of the t tables, "You are probably the only man who will ever use them (Box 1978)."<br><br />
<br />
“[W]e see the data analyst's insistence on ‘letting the data speak to us’ by plots and displays as an instinctive understanding of the need to encourage and to stimulate the pattern recognition and model generating capability of the right brain. Also, it expresses his concern that we not allow our pushy deductive left brain to take over too quickly and perhaps forcibly produce unwarranted conclusions based on an inadequate model.”<br />
<div align=right>George Box in “The Importance of Practice in the Development of Statistics”<br><br />
<i>Technometrics</i>, February 1984</div><br />
<br />
Thomas L. Moore recommended this article in an ISOSTAT posting. (It is available in [http://www.jstor.org/discover/10.2307/1268410?uid=3739952&uid=2129&uid=2134&uid=2&uid=70&uid=4&uid=3739256&sid=21100643590836 JSTOR].)<br />
<br />
----<br />
We are familiar with George Box’s famous statement: “All models are wrong but some are useful.” Here is another variant, cited in [http://en.wikiquote.org/wiki/George_E._P._Box Wikipedia]:<br />
:“Remember that all models are wrong; the practical question is how wrong do they have to be to not be useful.”<br />
<br />
Submitted by Margaret Cibes<br />
<br />
(Note. For an interesting discussion revisiting this theme, see [http://andrewgelman.com/2012/03/all-models-are-right-most-are-useless/ All models are right, most are useless] on Andrew Gelman's blog, 4 March 2012).<br />
----<br />
“There are two kinds of statistics: the kind you look up and the kind you make up.”<br />
<br />
<div align=right> --attributed to Rex Stout, American writer (1886 - 1975)</div><br />
Submitted by Paul Alper<br />
<br />
<br />
----<br />
<br />
“Definition of Statistics: The science of producing unreliable facts from reliable figures.”<br />
<br />
"The only science that enables different experts using the same figures to draw different conclusions."<br />
<br />
<div align=right> --attributed to Evan Esar, American humorist (1899–1995)</div><br />
Submitted by Paul Alper<br />
<br />
----<br />
“Science involves confronting our `absolute stupidity'. That kind of stupidity is an existential fact, inherent in our efforts to push our way into the unknown. …. Focusing on important questions puts us in the awkward position of being ignorant. One of the beautiful things about science is that it allows us to bumble along, getting it wrong time after time, and feel perfectly fine as long as we learn something each time. …. The more comfortable we become with being stupid, the deeper we will wade into the unknown and the more likely we are to make big discoveries.”<br />
<div align=right>UVa scientist Martin Schwartz in [http://jcs.biologists.org/content/121/11/1771.full “The importance of stupidity in scientific research”]<br><br />
<i>Journal of Cell Science</i>, 2008</div><br />
Submitted by Margaret Cibes<br />
<br />
==Forsooth==<br />
“In the first four months [at the new Resorts World Casino New York City], roughly 25,000 gamblers showed up every day, shoving a collective $2.3 billion through the slots and losing $140 million in the process. …. Resorts World offers more than 4,000 slot machines, but thanks to state law, there are no traditional card tables.”<br />
<div align=right>[http://online.wsj.com/article/SB10001424052970204792404577229481078006746.html?link=SM_clmst_sum#articleTabs%3Dcomments “The Gamblers’ New Game”]<br><br />
<i>The Wall Street Journal</i>, February 18, 2012</div><br />
Submitted by Margaret Cibes<br />
<br />
-----<br />
[[File:Elderly_drivers.jpg|400px]]<br><br><br />
“Drivers 85 and older still have a higher rate of deadly crashes than any other age group except teenagers.”<br><br />
<br />
(The article also describes two women who have learned to "compensate" for their macular degeneration in various ways - not necessarily welcome news!)<br />
<div align=right>[http://online.wsj.com/article/SB10001424052970204520204577251310179633818.html?KEYWORDS=sue+shellenbarger “Safer Over 70: Divers Keep the Keys”]<br><br />
<i>The Wall Street Journal</i>, February 29, 2012</div><br />
Submitted by Margaret Cibes<br />
<br />
-----<br />
[[File:PieChartMost.png|300px]]<br><br><br />
See the "Observation" at the top of the chart.<br />
<div align=right><br />
[http://junkcharts.typepad.com/junk_charts/2012/02/the-meaning-of-most.html “The Meaning of most”], downloaded from Junk Charts, March 1, 2012<br><br />
originally cited in [http://blog.kissmetrics.com/loading-time/?wide=1 “Mobile vs. Desktop”], KISSmetrics</div><br />
Submitted by Margaret Cibes<br />
<br />
-----<br />
<br />
“Here is the rub: Apple is so big, it’s running up against the law of large numbers. Also known as the golden theorem, with a proof attributed to the 17th-century Swiss mathematician Jacob Bernoulli, the law states that a variable will revert to a mean over a large sample of results. In the case of the largest companies, it suggests that high earnings growth and a rapid rise in share price will slow as those companies grow ever larger.”<br />
<div align=right>James Stewart in [http://www.nytimes.com/2012/02/25/business/apple-confronts-the-law-of-large-numbers-common-sense.html?_r=2&hp “Confronting a Law of Limits”]<br><br />
<i>The New York Times</i>, February 24, 2012</div><br />
Bill Peterson found Andrew Gelman’s comments[http://andrewgelman.com/2012/02/apple-confronts-the-law-of-large-numbers-huh/] about this article. <br />
<br />
Submitted by Margaret Cibes<br />
<br />
==Kaiser Fung on Minnesota’s ramp meters==<br />
<br />
A number references to Kaiser Fung’s book, ''Numbers Rule Your World'', appear in [http://test.causeweb.org/wiki/chance/index.php/Chance_News_82 Chance News 82]. From a Minnesotan’s point of view, however, the most important topic he discusses is not hurricanes, not drug testing, and not bias in standardized testing. Rather, the most critical issue is ramp metering as a means of improving traffic flow, relieving congestion and reducing travel time on Minnesota highways. “Industry experts regard Minnesota’s system of 430 ramp meters as a national model.”<br />
<br />
Unfortunately, “perception trumped reality.” An influential state senator, Dick Day, now a lobbyist for gambling interests, “led a charge to abolish the nationally recognized program, portraying it as part of the problem, not the solution.”<br />
<blockquote><br />
Leave it to Senator Day to speak the minds of “average Joes”--the people he meets at coffee shops, county fairs, summer parades, and the stock car races he loves. He saw ramp metering as a symbol of Big Government strangling our liberty.<br><br><br />
In the Twin Cities, drivers perceived their trip times to have lengthened [due to the ramp meters] even though in reality they have probably decreased. Thus, when in September 2000, the state legislature passed a mandate requiring MnDOT [Minnesota Department of Transportation] to conduct a “meters shutoff” experiment [of six weeks], the engineers [who devised the metering program] were stunned and disillusioned.<br />
</blockquote><br />
<br />
To make a long story short, when the ramp meters came back on, it turns out that: <br />
<blockquote><br />
[T]he engineering vision triumphed. Freeway conditions indeed worsened after the ramp meters went off. The key findings, based on actual measurements were as follows:<br />
*Peak freeway volume dropped by 9 percent.<br />
*Travel times rose by 22 percent, and the reliability deteriorated.<br />
*Travel speeds declined by 7 percent.<br />
*The number of crashes during merges jumped by 26 percent.<br />
</blockquote><br />
<br />
“The consultants further estimated that the benefits of ramp metering outweighed costs by five to one.” Nevertheless, the-above objective measures had to continue to battle subjective ones:<br />
<blockquote><br />
Despite the reality that commuters shortened their journeys if they waited their turns at the ramps, the drivers did not perceive the trade-off to be beneficial; they insisted that they would rather be moving slowly on the freeway than coming to a standstill at the ramp.<br />
</blockquote><br />
<br />
Accordingly, the engineers decided to modify the optimum solution to take into account driver psychology. “When they turned the lights back on, they limited waiting time on the ramps to four minutes, retired some unnecessary meters, and also shortened the operating hours.” Said differently, the constrained optimization model the engineers first considered left out some pivotal constraints.<br />
<br />
===Discussion===<br />
<br />
1. Do a search for “behavioral economics” to see the prevalence of irrational perceptions and subjective calculations in the economic sphere.<br />
<br />
2. Fung discusses an allied, albeit inverse, problem of waiting-time misconception. This instance concerns Disney World and its popular so-called FastPass as a means of avoiding queues. According to Fung<br />
<blockquote><br />
Clearly, FastPass users love the product--but how much waiting time can they save? Amazingly, the answer is none; they spend the same amount of time waiting for popular rides with or without FastPass!..So Disney confirms yet again that perception trumps reality. The FastPass concept is an absolute stroke of genius; it utterly changes perceived waiting times and has made many, many park-goers very, very giddy.<br />
</blockquote><br />
<br />
3. An oft-repeated and perhaps apocryphal operations research/statistics/decision theory anecdote has to do with elevators in a very large office building. Employees complained about excessive waiting times because the elevators all too frequently seemed to be in lockstep. Any physical solution such as creating a new elevator shaft or installing a complicated timing algorithm would be very expensive. The famous and utterly inexpensive psychological solution whereby perception trumped reality was to put in mirrors so that the waiting time would seem less because the employees would enjoy admiring themselves in the mirrors. Note that older and more benighted operations research/statistics/decision theory textbooks would have used the word “women” instead of “employees” in the previous sentence. <br />
<br />
4. A very modern and frustrating example of perception again trumping reality can often be observed in supermarkets which have installed self-checkout lanes without placing a limit on the number of items per shopper. In order to avoid a line at the regular checkout, some shoppers with an extremely large number of items will often choose the self-checkout and take much longer to finish than if had they queued at the regular checkout. Explain why said shoppers psychologically might prefer to persist in that behavior despite evidence to the contrary. Why don’t supermarkets simply limit the number of items per customer at self-checkout lanes?<br />
<br />
Submitted by Paul Alper<br />
<br />
==Don’t forget Chebyshev==<br />
<i>Super Crunchers</i>, by Ian Ayres, Random House, 2007<br><br />
<br />
<blockquote>When I taught at Stanford Law School, professors were required to award grades that had a 3.2 mean. …. The problem was that many of the students and many of the professors had no way to express the degree of variability in professors’ grading habits. …. As a nation, we lack a vocabulary of dispersion. We don’t know how to express what we intuitively know about the variability of a distribution of numbers. The 2SD [2 standard-deviation] rule could help give us this vocabulary. A professor who said that her standard deviation was .2 could have conveyed a lot of information with a single number. The problem is that very few people in the U.S. today understand what this means. But you should know and be able to explain to others that only about 2.5 percent of the professor’s grades are above 3.6. [pp. 221-222]</blockquote><br />
<br />
===Discussion===<br />
1. Suppose that a professor's <i>awarded</i> grades had mean 3.2 and SD 0.2.<br> <br />
(a) Under what condition could we say that “only about 2.5 percent of the professor’s grades are above 3.6”?<br><br />
(b) Without that condition, what could we say, if anything, about the percent of awarded grades <i>outside</i> of a 2SD range about the mean? About the percent of awarded grades <i>above</i> 3.6?<br><br />
2. Suppose that a professor's <i>raw grades</i> had mean 3.2 and SD 0.2. Do you think that this would be a realistic scenario in most undergraduate college classes? In most graduate-school classes? Why or why not?<br><br />
3. How could a professor construct a distribution of <i>awarded</i> grades with mean 3.2 and SD 0.2, based on <i>raw grades</i>, so that one could say that only about 2.5 percent of the <i>awarded</i> grades are above 3.6? What effect, if any, could that scaling have had on the worst – or on the best – raw grades?<br><br />
<br />
Submitted by Margaret Cibes<br />
<br />
==Critique of women-in-science statistics==<br />
[http://scholarship.claremont.edu/cgi/viewcontent.cgi?article=1041&context=jhm&sei-redir=1&referer=http%3A%2F%2Fwww.google.com%2Furl%3Fsa%3Dt%26rct%3Dj%26q%3Drumors%2520of%2520rarity%26source%3Dweb%26cd%3D1%26ved%3D0CCMQFjAA%26url%3Dhttp%253A%252F%252Fscholarship.claremont.edu%252Fcgi%252Fviewcontent.cgi%253Farticle%253D1041%2526context%253Djhm%26ei%3DugheT7arHMPV0QGzpfjkAw%26usg%3DAFQjCNGPJtXVTh10gldzpdqxaolp1Shd8Q#search=%22rumors%20rarity%22 “Rumors of Our Rarity are Greatly Exaggerated: Bad Statistics About Women in Science”]<br><br />
by Cathy Kessel, <i>Journal of Humanistic Mathematics</i>, July 2011<br><br />
<br />
Based on her apparently extensive and detailed study of reports about female-to-male ratios with respect to STEM abilities/careers, Kessel discusses three major problems with the statistics cited in them, as well as with the repetition of these questionable figures in subsequent academic and non-academic reports. <br />
<blockquote>Whatever their origins, statistics which are mislabeled, misinterpreted, fictitious, or otherwise defective remain in circulation because they are accepted by editors, readers, and referees.</blockquote><br />
<br />
“The Solitary Statistic.” A 13-to-1 boy-girl ratio in SAT-Math scores has been widely cited since it appeared in a 1983 <i>Science</i> article. That ratio was based on the scores of 280 seventh- and eighth-graders who scored 700 or above on the test over the period 1980-83. These students were part of a total of 64,000 students applying for a Johns Hopkins science program for exceptionally talented STEM-potential students. Kessel faults the widespread references to this outdated data, among other issues, and she cites more recent statistics at Hopkins and other such programs, including a ratio as low as 3 to 1 in 2005.<br><br />
<br />
“The Fabricated Statistic.” A “finding” that “Women talk almost three times as much as men” was published in <i>The Female Brain</i> in 2006. This was supposed to explain why women prefer careers which allow them to “connect and communicate” as opposed careers in science and engineering. Kessel outlines some issues that might make this explanation questionable.<br><br />
<br />
“The Garbled Statistic.” An example from “The Science of Sex Differences in Science and Mathematics,” published in <i>Psychological Science in the Public Interest</i> in 2007, was a report that women were “8.3% of tenure-track faculty at ‘elite’ mathematics departments.” A 2002 survey produced similar math data; that survey was based on the “top 50 departments.” These and other reports generally reported only the aggregate figure and not any of the raw data by rank. Kessel gives other examples in which raw data summary tables (which she had requested and received) would have been helpful to interpreting results.<br />
<blockquote>Although noticing mistakes may require numerical sophistication or knowledge of particular fields, accurate reporting of names, dates, and sources of<br />
statistics does not take much skill. At the very least, authors and research assistants can copy categories and sources as well as numbers. Editors can (and should) ask for sources.</blockquote><br />
===Discussion===<br />
1. Is there anything random about the group of students applying to a university’s program for talented students - or about the top SAT-M scorers in that group? Why are these important questions?<br><br />
2. Kessel quotes a statement that has been reported a number of times: “Women use 20,000 words per day, while men use 7,000." How do you think the researchers got these counts?<br><br />
3. Why might it be important to consider academic rank as a variable in analyzing the progress, or lack thereof, of women in obtaining university positions?<br><br />
4. Why might it be important to know more about the sponsorship of these studies – researcher affiliations, funding, <i>etc.</i>?<br><br />
<br />
Submitted by Margaret Cibes, based on a reference in March 2012 <i>College Mathematics Journal</i><br />
<br />
==Ethics study of social classes==<br />
[http://blogs.wsj.com/ideas-market/2012/02/27/study-high-social-class-predicts-unethical-behavior/tab/print/ “Study: High Social Class Predicts Unethical Behavior”]<br><br />
<i>The Wall Street Journal</i>, February 27, 2012<br><br />
<br />
Here is an abstract of the study[http://www.pnas.org/content/early/2012/02/21/1118373109.abstract] referred to in the article:<br />
<blockquote>Seven studies using experimental and naturalistic methods reveal that upper-class individuals behave more unethically than lower-class individuals. In studies 1 and 2, upper-class individuals were more likely to break the law while driving, relative to lower-class individuals. In follow-up laboratory studies, upper-class individuals were more likely to exhibit unethical decision-making tendencies (study 3), take valued goods from others (study 4), lie in a negotiation (study 5), cheat to increase their chances of winning a prize (study 6), and endorse unethical behavior at work (study 7) than were lower-class individuals. Mediator and moderator data demonstrated that upper-class individuals’ unethical tendencies are accounted for, in part, by their more favorable attitudes toward greed.</blockquote><br />
See also [http://www.pnas.org/content/suppl/2012/02/22/1118373109.DCSupplemental/pnas.201118373SI.pdf "Supporting Information"], published online in Proceedings of the National Academy of Sciences of the USA, February 27, 2012.<br><br />
<br />
===Discussion===<br />
1. If you were going to write an article about this study, and you had access to the entire report, what would be the first, most basic, information you would want to provide to your readers about the “class” categories referred to in the abstract?<br><br />
2. The article indicates that the sample sizes for the first three experiments were “250,” “150 drivers,” and “105 students.” Besides the relatively small sample sizes, what other issues can you identify as a potential problems in making any inference about ethics from these experimental results? <br />
<br />
Submitted by Margaret Cibes<br />
<br />
==Judea Pearl wins Turing Prize==<br />
Danny Kaplan posted a link to this story on the Isolated Statisicians e-mail list:<br />
<br />
[http://bits.blogs.nytimes.com/2012/03/15/a-turing-award-for-helping-make-computers-smarter/?hpw A Turing Award for helping make computers smarter].<br><br />
by Steve Lohr, Bits blog, ''New York Times'', 15 March 2012<br />
<br />
Judea Pearl of UCLA has been awarded this year's Turing Prize by the Association for Computing Machinery. According to the article Pearl's work on probabilistic reasoning and Bayesian networks has influenced applications in areas from search engines to fraud detection to speech recognition. The article includes testimonials from many noted experts in the field of artificial intelligence.<br />
<br />
Danny's message provided links to [http://bayes.cs.ucla.edu/jp_home.html Pearl's web page] for references to his work on causality, and to this [http://singapore.cs.ucla.edu/LECTURE/lecture_sec1.htm talk], which is the epilogue to his famous book, [http://bayes.cs.ucla.edu/BOOK-99/book-toc.html ''Causality''].<br />
<br />
Danny's [http://www.mosaic-web.org/go/StatisticalModeling ''Statistical Modeling'' textbook] includes a [http://www.mosaic-web.org/go/StatisticalModeling/Chapters/Chapter-17.pdf chapter] which discusses some of these ideas at a level appropriate for an introductory statistics audience.<br />
<br />
==A bizarre anatomical correlation==<br />
<br />
[http://www.nytimes.com/2012/03/17/opinion/collins-politicians-swinging-stethoscopes.html Politicians Swinging Stethoscopes], Gail Collins, The New York Times, March 16, 2012.<br />
<br />
When a topic carries strong emotions, often people forget to check their facts carefully. And abortion is possibly the most emotional topic in politics today. It's not too surprising that opponents of abortion have tried to promote a link between abortion and breast cancer.<br />
<br />
<blockquote>New Hampshire, for instance, seems to have developed a thing for linking sex and malignant disease. This week, the State House passed a bill that required that women who want to terminate a pregnancy be informed that abortions were linked to "an increased risk of breast cancer." As Terie Norelli, the minority leader, put it, the Legislature is attempting to make it a felony for a doctor "to not give a patient inaccurate information."</blockquote><br />
<br />
This was actually an issue [http://en.wikipedia.org/wiki/C._Everett_Koop#Government_service about 25 years ago, when C. Everett Koop was Surgeon General]. The American Cancer Society (ACS) has written that [http://www.cancer.org/Cancer/BreastCancer/MoreInformation/is-abortion-linked-to-breast-cancer scientific research studies have not found a cause-and-effect relationship between abortion and breast cancer], and cites a comprehensive review in 2003 by the National Cancer Institute (NCI). But numerous pro-life sites still claim the opposite, with headlines like [http://www.lifenews.com/2010/09/21/nat-6718/ Hundreds of Studies Confirm Abortion-Breast Cancer Link].<br />
<br />
It is interesting to speculate why pro-life sites would promote the abortion/breast cancer link so strongly in spite of dismissive commentary from respected organizations like ACS and NCI. If you believe that abortion is murder (as many people do), then it is not too far a leap to believe that something this evil would necessarily carry bad health consequences at the same time. It may be a belief that mainstream organizations like ACS and NCI are dominated by [http://www.wnd.com/2010/01/121749/ pro-abortion extremists].<br />
<br />
It is still curious, because if abortion is immoral, it should not matter whether it has bad side effects, such as an increased risk of breast cancer, or good side effects, [http://en.wikipedia.org/wiki/The_Impact_of_Legalized_Abortion_on_Crime such as a decrease in the crime rate]. It may be that opponents of abortion want to use whatever arguments they can to dissuade women from choosing abortion. It is worth noting that opponents of abortion are not the first and will not be the last group that has seized on a dubious statistical finding to support their political perspective.<br />
<br />
The abortion/breast cancer link at least has biological plausibility. The number of pregnancies that you have and the number of live births are indeed associated with various types of cancer, so it is not too far fetched to believe that abortion might be related to these cancers as well. But another cancer link in an area almost as contentious lacks even this biological plausibility.<br />
<br />
<blockquote>And there’s more. One of the sponsors, Representative Jeanine Notter, recently asked a colleague whether he would be interested, "as a man," to know that there was a study "that links the pill to prostate cancer."</blockquote><br />
<br />
Clearly, Ms. Notter understands that only women consume birth control pills and that only men have a prostate. What she is claiming is <br />
<br />
<blockquote>that nations with high use of birth control pills among women also tended to have high rates of prostate cancer among men.</blockquote><br />
<br />
Gail Collins mocks this correlation.<br />
<br />
<blockquote>You could also possibly discover that nations with the lowest per capita number of ferrets have a higher rate of prostate cancer.</blockquote><br />
<br />
Birth control pills are possibly associated with ovarian and cervical cancer, and these are two organs that women do have. They may also be associated with a decrease in the risk of endometrial cancer. If you don't believe that NCI is overrun by extremists, you might find [http://www.cancer.gov/cancertopics/factsheet/Risk/oral-contraceptives this fact sheet] to offer a helpful review of these risks. Disentangling these cancer risks from other confounders (such as the age at first sexual intercourse) is very difficult.<br />
<br />
===Questions===<br />
<br />
1. What is the name for the type of study that notes that "nations with high use of birth control pills among women also tended to have high rates of prostate cancer among men"?<br />
<br />
2. Randomized studies of the link between abortion and breast cancer are clearly impossible. What types of observational studies might be used to examine this link. What are the strengths and weaknesses of those types of studies.</div>Simon66217https://www.causeweb.org/wiki/chance/index.php?title=Chance_News_83&diff=15449Chance News 832012-03-19T16:20:18Z<p>Simon66217: /* A bizarre anatomical correlation */</p>
<hr />
<div>==Quotations==<br />
“A poll is not laser surgery; it’s an estimate.”<br />
<div align=right>ABC News polling director in [http://abcnews.go.com/blogs/politics/2007/12/moe-and-mojo/ “MOE and Mojo”]<br><br />
ABC Blogs, December 3, 2007</div><br />
Submitted by Margaret Cibes<br />
<br />
----<br />
"The most famous result of Student’s experimental method is Student’s t-table. But the real end of Student’s inquiry was taste, quality control, and minimally efficient sample sizes for experimental Guinness – not to achieve statistical significance at the .05 level or, worse yet, boast about an artificially randomized experiment."<br />
<br />
<div align=right>--Stephen T. Ziliak, in [http://sites.roosevelt.edu/sziliak/files/2012/02/William-S-Gosset-and-Experimental-Statistics-Ziliak-JWE-2011.pdf W.S. Gosset and some neglected concepts in experimental statistics: Guinnessometrics II]</div><br />
<br />
(Ziliak is the co-author of [http://www.press.umich.edu/titleDetailPraise.do?id=186351 ''The Cult of Statistical Significance: How the Standard Error Costs Us Jobs, Justice, and Lives''])<br />
<br />
Submitted by Bill Peterson<br />
<br />
-----<br />
“[W. S. Gossett] wrote to R. A. Fisher of the t tables, "You are probably the only man who will ever use them (Box 1978)."<br><br />
<br />
“[W]e see the data analyst's insistence on ‘letting the data speak to us’ by plots and displays as an instinctive understanding of the need to encourage and to stimulate the pattern recognition and model generating capability of the right brain. Also, it expresses his concern that we not allow our pushy deductive left brain to take over too quickly and perhaps forcibly produce unwarranted conclusions based on an inadequate model.”<br />
<div align=right>George Box in “The Importance of Practice in the Development of Statistics”<br><br />
<i>Technometrics</i>, February 1984</div><br />
<br />
Thomas L. Moore recommended this article in an ISOSTAT posting. (It is available in [http://www.jstor.org/discover/10.2307/1268410?uid=3739952&uid=2129&uid=2134&uid=2&uid=70&uid=4&uid=3739256&sid=21100643590836 JSTOR].)<br />
<br />
----<br />
We are familiar with George Box’s famous statement: “All models are wrong but some are useful.” Here is another variant, cited in [http://en.wikiquote.org/wiki/George_E._P._Box Wikipedia]:<br />
:“Remember that all models are wrong; the practical question is how wrong do they have to be to not be useful.”<br />
<br />
Submitted by Margaret Cibes<br />
<br />
(Note. For an interesting discussion revisiting this theme, see [http://andrewgelman.com/2012/03/all-models-are-right-most-are-useless/ All models are right, most are useless] on Andrew Gelman's blog, 4 March 2012).<br />
----<br />
“There are two kinds of statistics: the kind you look up and the kind you make up.”<br />
<br />
<div align=right> --attributed to Rex Stout, American writer (1886 - 1975)</div><br />
Submitted by Paul Alper<br />
<br />
<br />
----<br />
<br />
“Definition of Statistics: The science of producing unreliable facts from reliable figures.”<br />
<br />
"The only science that enables different experts using the same figures to draw different conclusions."<br />
<br />
<div align=right> --attributed to Evan Esar, American humorist (1899–1995)</div><br />
Submitted by Paul Alper<br />
<br />
----<br />
“Science involves confronting our `absolute stupidity'. That kind of stupidity is an existential fact, inherent in our efforts to push our way into the unknown. …. Focusing on important questions puts us in the awkward position of being ignorant. One of the beautiful things about science is that it allows us to bumble along, getting it wrong time after time, and feel perfectly fine as long as we learn something each time. …. The more comfortable we become with being stupid, the deeper we will wade into the unknown and the more likely we are to make big discoveries.”<br />
<div align=right>UVa scientist Martin Schwartz in [http://jcs.biologists.org/content/121/11/1771.full “The importance of stupidity in scientific research”]<br><br />
<i>Journal of Cell Science</i>, 2008</div><br />
Submitted by Margaret Cibes<br />
<br />
==Forsooth==<br />
“In the first four months [at the new Resorts World Casino New York City], roughly 25,000 gamblers showed up every day, shoving a collective $2.3 billion through the slots and losing $140 million in the process. …. Resorts World offers more than 4,000 slot machines, but thanks to state law, there are no traditional card tables.”<br />
<div align=right>[http://online.wsj.com/article/SB10001424052970204792404577229481078006746.html?link=SM_clmst_sum#articleTabs%3Dcomments “The Gamblers’ New Game”]<br><br />
<i>The Wall Street Journal</i>, February 18, 2012</div><br />
Submitted by Margaret Cibes<br />
<br />
-----<br />
[[File:Elderly_drivers.jpg|400px]]<br><br><br />
“Drivers 85 and older still have a higher rate of deadly crashes than any other age group except teenagers.”<br><br />
<br />
(The article also describes two women who have learned to "compensate" for their macular degeneration in various ways - not necessarily welcome news!)<br />
<div align=right>[http://online.wsj.com/article/SB10001424052970204520204577251310179633818.html?KEYWORDS=sue+shellenbarger “Safer Over 70: Divers Keep the Keys”]<br><br />
<i>The Wall Street Journal</i>, February 29, 2012</div><br />
Submitted by Margaret Cibes<br />
<br />
-----<br />
[[File:PieChartMost.png|300px]]<br><br><br />
See the "Observation" at the top of the chart.<br />
<div align=right><br />
[http://junkcharts.typepad.com/junk_charts/2012/02/the-meaning-of-most.html “The Meaning of most”], downloaded from Junk Charts, March 1, 2012<br><br />
originally cited in [http://blog.kissmetrics.com/loading-time/?wide=1 “Mobile vs. Desktop”], KISSmetrics</div><br />
Submitted by Margaret Cibes<br />
<br />
-----<br />
<br />
“Here is the rub: Apple is so big, it’s running up against the law of large numbers. Also known as the golden theorem, with a proof attributed to the 17th-century Swiss mathematician Jacob Bernoulli, the law states that a variable will revert to a mean over a large sample of results. In the case of the largest companies, it suggests that high earnings growth and a rapid rise in share price will slow as those companies grow ever larger.”<br />
<div align=right>James Stewart in [http://www.nytimes.com/2012/02/25/business/apple-confronts-the-law-of-large-numbers-common-sense.html?_r=2&hp “Confronting a Law of Limits”]<br><br />
<i>The New York Times</i>, February 24, 2012</div><br />
Bill Peterson found Andrew Gelman’s comments[http://andrewgelman.com/2012/02/apple-confronts-the-law-of-large-numbers-huh/] about this article. <br />
<br />
Submitted by Margaret Cibes<br />
<br />
==Kaiser Fung on Minnesota’s ramp meters==<br />
<br />
A number references to Kaiser Fung’s book, ''Numbers Rule Your World'', appear in [http://test.causeweb.org/wiki/chance/index.php/Chance_News_82 Chance News 82]. From a Minnesotan’s point of view, however, the most important topic he discusses is not hurricanes, not drug testing, and not bias in standardized testing. Rather, the most critical issue is ramp metering as a means of improving traffic flow, relieving congestion and reducing travel time on Minnesota highways. “Industry experts regard Minnesota’s system of 430 ramp meters as a national model.”<br />
<br />
Unfortunately, “perception trumped reality.” An influential state senator, Dick Day, now a lobbyist for gambling interests, “led a charge to abolish the nationally recognized program, portraying it as part of the problem, not the solution.”<br />
<blockquote><br />
Leave it to Senator Day to speak the minds of “average Joes”--the people he meets at coffee shops, county fairs, summer parades, and the stock car races he loves. He saw ramp metering as a symbol of Big Government strangling our liberty.<br><br><br />
In the Twin Cities, drivers perceived their trip times to have lengthened [due to the ramp meters] even though in reality they have probably decreased. Thus, when in September 2000, the state legislature passed a mandate requiring MnDOT [Minnesota Department of Transportation] to conduct a “meters shutoff” experiment [of six weeks], the engineers [who devised the metering program] were stunned and disillusioned.<br />
</blockquote><br />
<br />
To make a long story short, when the ramp meters came back on, it turns out that: <br />
<blockquote><br />
[T]he engineering vision triumphed. Freeway conditions indeed worsened after the ramp meters went off. The key findings, based on actual measurements were as follows:<br />
*Peak freeway volume dropped by 9 percent.<br />
*Travel times rose by 22 percent, and the reliability deteriorated.<br />
*Travel speeds declined by 7 percent.<br />
*The number of crashes during merges jumped by 26 percent.<br />
</blockquote><br />
<br />
“The consultants further estimated that the benefits of ramp metering outweighed costs by five to one.” Nevertheless, the-above objective measures had to continue to battle subjective ones:<br />
<blockquote><br />
Despite the reality that commuters shortened their journeys if they waited their turns at the ramps, the drivers did not perceive the trade-off to be beneficial; they insisted that they would rather be moving slowly on the freeway than coming to a standstill at the ramp.<br />
</blockquote><br />
<br />
Accordingly, the engineers decided to modify the optimum solution to take into account driver psychology. “When they turned the lights back on, they limited waiting time on the ramps to four minutes, retired some unnecessary meters, and also shortened the operating hours.” Said differently, the constrained optimization model the engineers first considered left out some pivotal constraints.<br />
<br />
===Discussion===<br />
<br />
1. Do a search for “behavioral economics” to see the prevalence of irrational perceptions and subjective calculations in the economic sphere.<br />
<br />
2. Fung discusses an allied, albeit inverse, problem of waiting-time misconception. This instance concerns Disney World and its popular so-called FastPass as a means of avoiding queues. According to Fung<br />
<blockquote><br />
Clearly, FastPass users love the product--but how much waiting time can they save? Amazingly, the answer is none; they spend the same amount of time waiting for popular rides with or without FastPass!..So Disney confirms yet again that perception trumps reality. The FastPass concept is an absolute stroke of genius; it utterly changes perceived waiting times and has made many, many park-goers very, very giddy.<br />
</blockquote><br />
<br />
3. An oft-repeated and perhaps apocryphal operations research/statistics/decision theory anecdote has to do with elevators in a very large office building. Employees complained about excessive waiting times because the elevators all too frequently seemed to be in lockstep. Any physical solution such as creating a new elevator shaft or installing a complicated timing algorithm would be very expensive. The famous and utterly inexpensive psychological solution whereby perception trumped reality was to put in mirrors so that the waiting time would seem less because the employees would enjoy admiring themselves in the mirrors. Note that older and more benighted operations research/statistics/decision theory textbooks would have used the word “women” instead of “employees” in the previous sentence. <br />
<br />
4. A very modern and frustrating example of perception again trumping reality can often be observed in supermarkets which have installed self-checkout lanes without placing a limit on the number of items per shopper. In order to avoid a line at the regular checkout, some shoppers with an extremely large number of items will often choose the self-checkout and take much longer to finish than if had they queued at the regular checkout. Explain why said shoppers psychologically might prefer to persist in that behavior despite evidence to the contrary. Why don’t supermarkets simply limit the number of items per customer at self-checkout lanes?<br />
<br />
Submitted by Paul Alper<br />
<br />
==Don’t forget Chebyshev==<br />
<i>Super Crunchers</i>, by Ian Ayres, Random House, 2007<br><br />
<br />
<blockquote>When I taught at Stanford Law School, professors were required to award grades that had a 3.2 mean. …. The problem was that many of the students and many of the professors had no way to express the degree of variability in professors’ grading habits. …. As a nation, we lack a vocabulary of dispersion. We don’t know how to express what we intuitively know about the variability of a distribution of numbers. The 2SD [2 standard-deviation] rule could help give us this vocabulary. A professor who said that her standard deviation was .2 could have conveyed a lot of information with a single number. The problem is that very few people in the U.S. today understand what this means. But you should know and be able to explain to others that only about 2.5 percent of the professor’s grades are above 3.6. [pp. 221-222]</blockquote><br />
<br />
===Discussion===<br />
1. Suppose that a professor's <i>awarded</i> grades had mean 3.2 and SD 0.2.<br> <br />
(a) Under what condition could we say that “only about 2.5 percent of the professor’s grades are above 3.6”?<br><br />
(b) Without that condition, what could we say, if anything, about the percent of awarded grades <i>outside</i> of a 2SD range about the mean? About the percent of awarded grades <i>above</i> 3.6?<br><br />
2. Suppose that a professor's <i>raw grades</i> had mean 3.2 and SD 0.2. Do you think that this would be a realistic scenario in most undergraduate college classes? In most graduate-school classes? Why or why not?<br><br />
3. How could a professor construct a distribution of <i>awarded</i> grades with mean 3.2 and SD 0.2, based on <i>raw grades</i>, so that one could say that only about 2.5 percent of the <i>awarded</i> grades are above 3.6? What effect, if any, could that scaling have had on the worst – or on the best – raw grades?<br><br />
<br />
Submitted by Margaret Cibes<br />
<br />
==Critique of women-in-science statistics==<br />
[http://scholarship.claremont.edu/cgi/viewcontent.cgi?article=1041&context=jhm&sei-redir=1&referer=http%3A%2F%2Fwww.google.com%2Furl%3Fsa%3Dt%26rct%3Dj%26q%3Drumors%2520of%2520rarity%26source%3Dweb%26cd%3D1%26ved%3D0CCMQFjAA%26url%3Dhttp%253A%252F%252Fscholarship.claremont.edu%252Fcgi%252Fviewcontent.cgi%253Farticle%253D1041%2526context%253Djhm%26ei%3DugheT7arHMPV0QGzpfjkAw%26usg%3DAFQjCNGPJtXVTh10gldzpdqxaolp1Shd8Q#search=%22rumors%20rarity%22 “Rumors of Our Rarity are Greatly Exaggerated: Bad Statistics About Women in Science”]<br><br />
by Cathy Kessel, <i>Journal of Humanistic Mathematics</i>, July 2011<br><br />
<br />
Based on her apparently extensive and detailed study of reports about female-to-male ratios with respect to STEM abilities/careers, Kessel discusses three major problems with the statistics cited in them, as well as with the repetition of these questionable figures in subsequent academic and non-academic reports. <br />
<blockquote>Whatever their origins, statistics which are mislabeled, misinterpreted, fictitious, or otherwise defective remain in circulation because they are accepted by editors, readers, and referees.</blockquote><br />
<br />
“The Solitary Statistic.” A 13-to-1 boy-girl ratio in SAT-Math scores has been widely cited since it appeared in a 1983 <i>Science</i> article. That ratio was based on the scores of 280 seventh- and eighth-graders who scored 700 or above on the test over the period 1980-83. These students were part of a total of 64,000 students applying for a Johns Hopkins science program for exceptionally talented STEM-potential students. Kessel faults the widespread references to this outdated data, among other issues, and she cites more recent statistics at Hopkins and other such programs, including a ratio as low as 3 to 1 in 2005.<br><br />
<br />
“The Fabricated Statistic.” A “finding” that “Women talk almost three times as much as men” was published in <i>The Female Brain</i> in 2006. This was supposed to explain why women prefer careers which allow them to “connect and communicate” as opposed careers in science and engineering. Kessel outlines some issues that might make this explanation questionable.<br><br />
<br />
“The Garbled Statistic.” An example from “The Science of Sex Differences in Science and Mathematics,” published in <i>Psychological Science in the Public Interest</i> in 2007, was a report that women were “8.3% of tenure-track faculty at ‘elite’ mathematics departments.” A 2002 survey produced similar math data; that survey was based on the “top 50 departments.” These and other reports generally reported only the aggregate figure and not any of the raw data by rank. Kessel gives other examples in which raw data summary tables (which she had requested and received) would have been helpful to interpreting results.<br />
<blockquote>Although noticing mistakes may require numerical sophistication or knowledge of particular fields, accurate reporting of names, dates, and sources of<br />
statistics does not take much skill. At the very least, authors and research assistants can copy categories and sources as well as numbers. Editors can (and should) ask for sources.</blockquote><br />
===Discussion===<br />
1. Is there anything random about the group of students applying to a university’s program for talented students - or about the top SAT-M scorers in that group? Why are these important questions?<br><br />
2. Kessel quotes a statement that has been reported a number of times: “Women use 20,000 words per day, while men use 7,000." How do you think the researchers got these counts?<br><br />
3. Why might it be important to consider academic rank as a variable in analyzing the progress, or lack thereof, of women in obtaining university positions?<br><br />
4. Why might it be important to know more about the sponsorship of these studies – researcher affiliations, funding, <i>etc.</i>?<br><br />
<br />
Submitted by Margaret Cibes, based on a reference in March 2012 <i>College Mathematics Journal</i><br />
<br />
==Ethics study of social classes==<br />
[http://blogs.wsj.com/ideas-market/2012/02/27/study-high-social-class-predicts-unethical-behavior/tab/print/ “Study: High Social Class Predicts Unethical Behavior”]<br><br />
<i>The Wall Street Journal</i>, February 27, 2012<br><br />
<br />
Here is an abstract of the study[http://www.pnas.org/content/early/2012/02/21/1118373109.abstract] referred to in the article:<br />
<blockquote>Seven studies using experimental and naturalistic methods reveal that upper-class individuals behave more unethically than lower-class individuals. In studies 1 and 2, upper-class individuals were more likely to break the law while driving, relative to lower-class individuals. In follow-up laboratory studies, upper-class individuals were more likely to exhibit unethical decision-making tendencies (study 3), take valued goods from others (study 4), lie in a negotiation (study 5), cheat to increase their chances of winning a prize (study 6), and endorse unethical behavior at work (study 7) than were lower-class individuals. Mediator and moderator data demonstrated that upper-class individuals’ unethical tendencies are accounted for, in part, by their more favorable attitudes toward greed.</blockquote><br />
See also [http://www.pnas.org/content/suppl/2012/02/22/1118373109.DCSupplemental/pnas.201118373SI.pdf "Supporting Information"], published online in Proceedings of the National Academy of Sciences of the USA, February 27, 2012.<br><br />
<br />
===Discussion===<br />
1. If you were going to write an article about this study, and you had access to the entire report, what would be the first, most basic, information you would want to provide to your readers about the “class” categories referred to in the abstract?<br><br />
2. The article indicates that the sample sizes for the first three experiments were “250,” “150 drivers,” and “105 students.” Besides the relatively small sample sizes, what other issues can you identify as a potential problems in making any inference about ethics from these experimental results? <br />
<br />
Submitted by Margaret Cibes<br />
<br />
==Judea Pearl wins Turing Prize==<br />
Danny Kaplan posted a link to this story on the Isolated Statisicians e-mail list:<br />
<br />
[http://bits.blogs.nytimes.com/2012/03/15/a-turing-award-for-helping-make-computers-smarter/?hpw A Turing Award for helping make computers smarter].<br><br />
by Steve Lohr, Bits blog, ''New York Times'', 15 March 2012<br />
<br />
Judea Pearl of UCLA has been awarded this year's Turing Prize by the Association for Computing Machinery. According to the article Pearl's work on probabilistic reasoning and Bayesian networks has influenced applications in areas from search engines to fraud detection to speech recognition. The article includes testimonials from many noted experts in the field of artificial intelligence.<br />
<br />
Danny's message provided links to [http://bayes.cs.ucla.edu/jp_home.html Pearl's web page] for references to his work on causality, and to this [http://singapore.cs.ucla.edu/LECTURE/lecture_sec1.htm talk], which is the epilogue to his famous book, [http://bayes.cs.ucla.edu/BOOK-99/book-toc.html ''Causality''].<br />
<br />
Danny's [http://www.mosaic-web.org/go/StatisticalModeling ''Statistical Modeling'' textbook] includes a [http://www.mosaic-web.org/go/StatisticalModeling/Chapters/Chapter-17.pdf chapter] which discusses some of these ideas at a level appropriate for an introductory statistics audience.<br />
<br />
==A bizarre anatomical correlation==<br />
<br />
[http://www.nytimes.com/2012/03/17/opinion/collins-politicians-swinging-stethoscopes.html Politicians Swinging Stethoscopes], Gail Collins, The New York Times, March 16, 2012.<br />
<br />
When a topic carries strong emotions, often people forget to check their facts carefully. And abortion is possibly the most emotional topic in politics today. It's not too surprising that opponents of abortion have tried to promote a link between abortion and breast cancer.<br />
<br />
<blockquote>New Hampshire, for instance, seems to have developed a thing for linking sex and malignant disease. This week, the State House passed a bill that required that women who want to terminate a pregnancy be informed that abortions were linked to "an increased risk of breast cancer." As Terie Norelli, the minority leader, put it, the Legislature is attempting to make it a felony for a doctor "to not give a patient inaccurate information."</blockquote><br />
<br />
This was actually an issue [http://en.wikipedia.org/wiki/C._Everett_Koop#Government_service about 25 years ago, when C. Everett Koop was Surgeon General]. The American Cancer Society (ACS) was written that [http://www.cancer.org/Cancer/BreastCancer/MoreInformation/is-abortion-linked-to-breast-cancer scientific research studies have not found a cause-and-effect relationship between abortion and breast cancer], and cites a comprehensive rview in 2003 by theh National Cancer Institute (NCI). But numerous pro-life sites still claim the opposite, with headlines like [http://www.lifenews.com/2010/09/21/nat-6718/ Hundreds of Studies Confirm Abortion-Breast Cancer Link].<br />
<br />
It is interesting to speculate why pro-life sites would promote the abortion/breast cancer link so strongly in spite of dismissive commentary from respected organizations like ACS and NCI. If you believe that abortion is murder (as many people do), then it is not too far a leap to believe that something this evil would necessarily carry bad health consequences at the same time. It may be a belief that mainstream organizations like ACS and NCI are dominated by [http://www.wnd.com/2010/01/121749/ pro-abortion extremists].<br />
<br />
It is still curious, because if abortion is immoral, it should not matter whether it has bad side effects, such as an increased risk of breast cancer, or good side effects, [http://en.wikipedia.org/wiki/The_Impact_of_Legalized_Abortion_on_Crime such as a decrease in the crime rate]. It may be that opponents of abortion want to use whatever arguments they can to dissuade women from choosing abortion.<br />
<br />
The abortion/breast cancer link at least has biological plausibility, but another cancer link in an area almost as contentious lacks even this biological plausibility.<br />
<br />
<blockquote>And there’s more. One of the sponsors, Representative Jeanine Notter, recently asked a colleague whether he would be interested, "as a man," to know that there was a study "that links the pill to prostate cancer."</blockquote><br />
<br />
Clearly, Ms. Notter understands that only women consume birth control pills and that only men have a prostate. What she is claiming is <br />
<br />
<blockquote>that nations with high use of birth control pills among women also tended to have high rates of prostate cancer among men.</blockquote><br />
<br />
Gail Collins mocks this correlation.<br />
<br />
<blockquote>You could also possibly discover that nations with the lowest per capita number of ferrets have a higher rate of prostate cancer.</blockquote><br />
<br />
===Questions===<br />
<br />
1. What is the name for the type of study that notes that "nations with high use of birth control pills among women also tended to have high rates of prostate cancer among men"?<br />
<br />
2. Randomized studies of the link between abortion and breast cancer are clearly impossible. What types of observational studies might be used to examine this link. What are the strengths and weaknesses of those types of studies.</div>Simon66217https://www.causeweb.org/wiki/chance/index.php?title=Chance_News_83&diff=15448Chance News 832012-03-19T16:13:10Z<p>Simon66217: /* A bizarre anatomical correlation */</p>
<hr />
<div>==Quotations==<br />
“A poll is not laser surgery; it’s an estimate.”<br />
<div align=right>ABC News polling director in [http://abcnews.go.com/blogs/politics/2007/12/moe-and-mojo/ “MOE and Mojo”]<br><br />
ABC Blogs, December 3, 2007</div><br />
Submitted by Margaret Cibes<br />
<br />
----<br />
"The most famous result of Student’s experimental method is Student’s t-table. But the real end of Student’s inquiry was taste, quality control, and minimally efficient sample sizes for experimental Guinness – not to achieve statistical significance at the .05 level or, worse yet, boast about an artificially randomized experiment."<br />
<br />
<div align=right>--Stephen T. Ziliak, in [http://sites.roosevelt.edu/sziliak/files/2012/02/William-S-Gosset-and-Experimental-Statistics-Ziliak-JWE-2011.pdf W.S. Gosset and some neglected concepts in experimental statistics: Guinnessometrics II]</div><br />
<br />
(Ziliak is the co-author of [http://www.press.umich.edu/titleDetailPraise.do?id=186351 ''The Cult of Statistical Significance: How the Standard Error Costs Us Jobs, Justice, and Lives''])<br />
<br />
Submitted by Bill Peterson<br />
<br />
-----<br />
“[W. S. Gossett] wrote to R. A. Fisher of the t tables, "You are probably the only man who will ever use them (Box 1978)."<br><br />
<br />
“[W]e see the data analyst's insistence on ‘letting the data speak to us’ by plots and displays as an instinctive understanding of the need to encourage and to stimulate the pattern recognition and model generating capability of the right brain. Also, it expresses his concern that we not allow our pushy deductive left brain to take over too quickly and perhaps forcibly produce unwarranted conclusions based on an inadequate model.”<br />
<div align=right>George Box in “The Importance of Practice in the Development of Statistics”<br><br />
<i>Technometrics</i>, February 1984</div><br />
<br />
Thomas L. Moore recommended this article in an ISOSTAT posting. (It is available in [http://www.jstor.org/discover/10.2307/1268410?uid=3739952&uid=2129&uid=2134&uid=2&uid=70&uid=4&uid=3739256&sid=21100643590836 JSTOR].)<br />
<br />
----<br />
We are familiar with George Box’s famous statement: “All models are wrong but some are useful.” Here is another variant, cited in [http://en.wikiquote.org/wiki/George_E._P._Box Wikipedia]:<br />
:“Remember that all models are wrong; the practical question is how wrong do they have to be to not be useful.”<br />
<br />
Submitted by Margaret Cibes<br />
<br />
(Note. For an interesting discussion revisiting this theme, see [http://andrewgelman.com/2012/03/all-models-are-right-most-are-useless/ All models are right, most are useless] on Andrew Gelman's blog, 4 March 2012).<br />
----<br />
“There are two kinds of statistics: the kind you look up and the kind you make up.”<br />
<br />
<div align=right> --attributed to Rex Stout, American writer (1886 - 1975)</div><br />
Submitted by Paul Alper<br />
<br />
<br />
----<br />
<br />
“Definition of Statistics: The science of producing unreliable facts from reliable figures.”<br />
<br />
"The only science that enables different experts using the same figures to draw different conclusions."<br />
<br />
<div align=right> --attributed to Evan Esar, American humorist (1899–1995)</div><br />
Submitted by Paul Alper<br />
<br />
----<br />
“Science involves confronting our `absolute stupidity'. That kind of stupidity is an existential fact, inherent in our efforts to push our way into the unknown. …. Focusing on important questions puts us in the awkward position of being ignorant. One of the beautiful things about science is that it allows us to bumble along, getting it wrong time after time, and feel perfectly fine as long as we learn something each time. …. The more comfortable we become with being stupid, the deeper we will wade into the unknown and the more likely we are to make big discoveries.”<br />
<div align=right>UVa scientist Martin Schwartz in [http://jcs.biologists.org/content/121/11/1771.full “The importance of stupidity in scientific research”]<br><br />
<i>Journal of Cell Science</i>, 2008</div><br />
Submitted by Margaret Cibes<br />
<br />
==Forsooth==<br />
“In the first four months [at the new Resorts World Casino New York City], roughly 25,000 gamblers showed up every day, shoving a collective $2.3 billion through the slots and losing $140 million in the process. …. Resorts World offers more than 4,000 slot machines, but thanks to state law, there are no traditional card tables.”<br />
<div align=right>[http://online.wsj.com/article/SB10001424052970204792404577229481078006746.html?link=SM_clmst_sum#articleTabs%3Dcomments “The Gamblers’ New Game”]<br><br />
<i>The Wall Street Journal</i>, February 18, 2012</div><br />
Submitted by Margaret Cibes<br />
<br />
-----<br />
[[File:Elderly_drivers.jpg|400px]]<br><br><br />
“Drivers 85 and older still have a higher rate of deadly crashes than any other age group except teenagers.”<br><br />
<br />
(The article also describes two women who have learned to "compensate" for their macular degeneration in various ways - not necessarily welcome news!)<br />
<div align=right>[http://online.wsj.com/article/SB10001424052970204520204577251310179633818.html?KEYWORDS=sue+shellenbarger “Safer Over 70: Divers Keep the Keys”]<br><br />
<i>The Wall Street Journal</i>, February 29, 2012</div><br />
Submitted by Margaret Cibes<br />
<br />
-----<br />
[[File:PieChartMost.png|300px]]<br><br><br />
See the "Observation" at the top of the chart.<br />
<div align=right><br />
[http://junkcharts.typepad.com/junk_charts/2012/02/the-meaning-of-most.html “The Meaning of most”], downloaded from Junk Charts, March 1, 2012<br><br />
originally cited in [http://blog.kissmetrics.com/loading-time/?wide=1 “Mobile vs. Desktop”], KISSmetrics</div><br />
Submitted by Margaret Cibes<br />
<br />
-----<br />
<br />
“Here is the rub: Apple is so big, it’s running up against the law of large numbers. Also known as the golden theorem, with a proof attributed to the 17th-century Swiss mathematician Jacob Bernoulli, the law states that a variable will revert to a mean over a large sample of results. In the case of the largest companies, it suggests that high earnings growth and a rapid rise in share price will slow as those companies grow ever larger.”<br />
<div align=right>James Stewart in [http://www.nytimes.com/2012/02/25/business/apple-confronts-the-law-of-large-numbers-common-sense.html?_r=2&hp “Confronting a Law of Limits”]<br><br />
<i>The New York Times</i>, February 24, 2012</div><br />
Bill Peterson found Andrew Gelman’s comments[http://andrewgelman.com/2012/02/apple-confronts-the-law-of-large-numbers-huh/] about this article. <br />
<br />
Submitted by Margaret Cibes<br />
<br />
==Kaiser Fung on Minnesota’s ramp meters==<br />
<br />
A number references to Kaiser Fung’s book, ''Numbers Rule Your World'', appear in [http://test.causeweb.org/wiki/chance/index.php/Chance_News_82 Chance News 82]. From a Minnesotan’s point of view, however, the most important topic he discusses is not hurricanes, not drug testing, and not bias in standardized testing. Rather, the most critical issue is ramp metering as a means of improving traffic flow, relieving congestion and reducing travel time on Minnesota highways. “Industry experts regard Minnesota’s system of 430 ramp meters as a national model.”<br />
<br />
Unfortunately, “perception trumped reality.” An influential state senator, Dick Day, now a lobbyist for gambling interests, “led a charge to abolish the nationally recognized program, portraying it as part of the problem, not the solution.”<br />
<blockquote><br />
Leave it to Senator Day to speak the minds of “average Joes”--the people he meets at coffee shops, county fairs, summer parades, and the stock car races he loves. He saw ramp metering as a symbol of Big Government strangling our liberty.<br><br><br />
In the Twin Cities, drivers perceived their trip times to have lengthened [due to the ramp meters] even though in reality they have probably decreased. Thus, when in September 2000, the state legislature passed a mandate requiring MnDOT [Minnesota Department of Transportation] to conduct a “meters shutoff” experiment [of six weeks], the engineers [who devised the metering program] were stunned and disillusioned.<br />
</blockquote><br />
<br />
To make a long story short, when the ramp meters came back on, it turns out that: <br />
<blockquote><br />
[T]he engineering vision triumphed. Freeway conditions indeed worsened after the ramp meters went off. The key findings, based on actual measurements were as follows:<br />
*Peak freeway volume dropped by 9 percent.<br />
*Travel times rose by 22 percent, and the reliability deteriorated.<br />
*Travel speeds declined by 7 percent.<br />
*The number of crashes during merges jumped by 26 percent.<br />
</blockquote><br />
<br />
“The consultants further estimated that the benefits of ramp metering outweighed costs by five to one.” Nevertheless, the-above objective measures had to continue to battle subjective ones:<br />
<blockquote><br />
Despite the reality that commuters shortened their journeys if they waited their turns at the ramps, the drivers did not perceive the trade-off to be beneficial; they insisted that they would rather be moving slowly on the freeway than coming to a standstill at the ramp.<br />
</blockquote><br />
<br />
Accordingly, the engineers decided to modify the optimum solution to take into account driver psychology. “When they turned the lights back on, they limited waiting time on the ramps to four minutes, retired some unnecessary meters, and also shortened the operating hours.” Said differently, the constrained optimization model the engineers first considered left out some pivotal constraints.<br />
<br />
===Discussion===<br />
<br />
1. Do a search for “behavioral economics” to see the prevalence of irrational perceptions and subjective calculations in the economic sphere.<br />
<br />
2. Fung discusses an allied, albeit inverse, problem of waiting-time misconception. This instance concerns Disney World and its popular so-called FastPass as a means of avoiding queues. According to Fung<br />
<blockquote><br />
Clearly, FastPass users love the product--but how much waiting time can they save? Amazingly, the answer is none; they spend the same amount of time waiting for popular rides with or without FastPass!..So Disney confirms yet again that perception trumps reality. The FastPass concept is an absolute stroke of genius; it utterly changes perceived waiting times and has made many, many park-goers very, very giddy.<br />
</blockquote><br />
<br />
3. An oft-repeated and perhaps apocryphal operations research/statistics/decision theory anecdote has to do with elevators in a very large office building. Employees complained about excessive waiting times because the elevators all too frequently seemed to be in lockstep. Any physical solution such as creating a new elevator shaft or installing a complicated timing algorithm would be very expensive. The famous and utterly inexpensive psychological solution whereby perception trumped reality was to put in mirrors so that the waiting time would seem less because the employees would enjoy admiring themselves in the mirrors. Note that older and more benighted operations research/statistics/decision theory textbooks would have used the word “women” instead of “employees” in the previous sentence. <br />
<br />
4. A very modern and frustrating example of perception again trumping reality can often be observed in supermarkets which have installed self-checkout lanes without placing a limit on the number of items per shopper. In order to avoid a line at the regular checkout, some shoppers with an extremely large number of items will often choose the self-checkout and take much longer to finish than if had they queued at the regular checkout. Explain why said shoppers psychologically might prefer to persist in that behavior despite evidence to the contrary. Why don’t supermarkets simply limit the number of items per customer at self-checkout lanes?<br />
<br />
Submitted by Paul Alper<br />
<br />
==Don’t forget Chebyshev==<br />
<i>Super Crunchers</i>, by Ian Ayres, Random House, 2007<br><br />
<br />
<blockquote>When I taught at Stanford Law School, professors were required to award grades that had a 3.2 mean. …. The problem was that many of the students and many of the professors had no way to express the degree of variability in professors’ grading habits. …. As a nation, we lack a vocabulary of dispersion. We don’t know how to express what we intuitively know about the variability of a distribution of numbers. The 2SD [2 standard-deviation] rule could help give us this vocabulary. A professor who said that her standard deviation was .2 could have conveyed a lot of information with a single number. The problem is that very few people in the U.S. today understand what this means. But you should know and be able to explain to others that only about 2.5 percent of the professor’s grades are above 3.6. [pp. 221-222]</blockquote><br />
<br />
===Discussion===<br />
1. Suppose that a professor's <i>awarded</i> grades had mean 3.2 and SD 0.2.<br> <br />
(a) Under what condition could we say that “only about 2.5 percent of the professor’s grades are above 3.6”?<br><br />
(b) Without that condition, what could we say, if anything, about the percent of awarded grades <i>outside</i> of a 2SD range about the mean? About the percent of awarded grades <i>above</i> 3.6?<br><br />
2. Suppose that a professor's <i>raw grades</i> had mean 3.2 and SD 0.2. Do you think that this would be a realistic scenario in most undergraduate college classes? In most graduate-school classes? Why or why not?<br><br />
3. How could a professor construct a distribution of <i>awarded</i> grades with mean 3.2 and SD 0.2, based on <i>raw grades</i>, so that one could say that only about 2.5 percent of the <i>awarded</i> grades are above 3.6? What effect, if any, could that scaling have had on the worst – or on the best – raw grades?<br><br />
<br />
Submitted by Margaret Cibes<br />
<br />
==Critique of women-in-science statistics==<br />
[http://scholarship.claremont.edu/cgi/viewcontent.cgi?article=1041&context=jhm&sei-redir=1&referer=http%3A%2F%2Fwww.google.com%2Furl%3Fsa%3Dt%26rct%3Dj%26q%3Drumors%2520of%2520rarity%26source%3Dweb%26cd%3D1%26ved%3D0CCMQFjAA%26url%3Dhttp%253A%252F%252Fscholarship.claremont.edu%252Fcgi%252Fviewcontent.cgi%253Farticle%253D1041%2526context%253Djhm%26ei%3DugheT7arHMPV0QGzpfjkAw%26usg%3DAFQjCNGPJtXVTh10gldzpdqxaolp1Shd8Q#search=%22rumors%20rarity%22 “Rumors of Our Rarity are Greatly Exaggerated: Bad Statistics About Women in Science”]<br><br />
by Cathy Kessel, <i>Journal of Humanistic Mathematics</i>, July 2011<br><br />
<br />
Based on her apparently extensive and detailed study of reports about female-to-male ratios with respect to STEM abilities/careers, Kessel discusses three major problems with the statistics cited in them, as well as with the repetition of these questionable figures in subsequent academic and non-academic reports. <br />
<blockquote>Whatever their origins, statistics which are mislabeled, misinterpreted, fictitious, or otherwise defective remain in circulation because they are accepted by editors, readers, and referees.</blockquote><br />
<br />
“The Solitary Statistic.” A 13-to-1 boy-girl ratio in SAT-Math scores has been widely cited since it appeared in a 1983 <i>Science</i> article. That ratio was based on the scores of 280 seventh- and eighth-graders who scored 700 or above on the test over the period 1980-83. These students were part of a total of 64,000 students applying for a Johns Hopkins science program for exceptionally talented STEM-potential students. Kessel faults the widespread references to this outdated data, among other issues, and she cites more recent statistics at Hopkins and other such programs, including a ratio as low as 3 to 1 in 2005.<br><br />
<br />
“The Fabricated Statistic.” A “finding” that “Women talk almost three times as much as men” was published in <i>The Female Brain</i> in 2006. This was supposed to explain why women prefer careers which allow them to “connect and communicate” as opposed careers in science and engineering. Kessel outlines some issues that might make this explanation questionable.<br><br />
<br />
“The Garbled Statistic.” An example from “The Science of Sex Differences in Science and Mathematics,” published in <i>Psychological Science in the Public Interest</i> in 2007, was a report that women were “8.3% of tenure-track faculty at ‘elite’ mathematics departments.” A 2002 survey produced similar math data; that survey was based on the “top 50 departments.” These and other reports generally reported only the aggregate figure and not any of the raw data by rank. Kessel gives other examples in which raw data summary tables (which she had requested and received) would have been helpful to interpreting results.<br />
<blockquote>Although noticing mistakes may require numerical sophistication or knowledge of particular fields, accurate reporting of names, dates, and sources of<br />
statistics does not take much skill. At the very least, authors and research assistants can copy categories and sources as well as numbers. Editors can (and should) ask for sources.</blockquote><br />
===Discussion===<br />
1. Is there anything random about the group of students applying to a university’s program for talented students - or about the top SAT-M scorers in that group? Why are these important questions?<br><br />
2. Kessel quotes a statement that has been reported a number of times: “Women use 20,000 words per day, while men use 7,000." How do you think the researchers got these counts?<br><br />
3. Why might it be important to consider academic rank as a variable in analyzing the progress, or lack thereof, of women in obtaining university positions?<br><br />
4. Why might it be important to know more about the sponsorship of these studies – researcher affiliations, funding, <i>etc.</i>?<br><br />
<br />
Submitted by Margaret Cibes, based on a reference in March 2012 <i>College Mathematics Journal</i><br />
<br />
==Ethics study of social classes==<br />
[http://blogs.wsj.com/ideas-market/2012/02/27/study-high-social-class-predicts-unethical-behavior/tab/print/ “Study: High Social Class Predicts Unethical Behavior”]<br><br />
<i>The Wall Street Journal</i>, February 27, 2012<br><br />
<br />
Here is an abstract of the study[http://www.pnas.org/content/early/2012/02/21/1118373109.abstract] referred to in the article:<br />
<blockquote>Seven studies using experimental and naturalistic methods reveal that upper-class individuals behave more unethically than lower-class individuals. In studies 1 and 2, upper-class individuals were more likely to break the law while driving, relative to lower-class individuals. In follow-up laboratory studies, upper-class individuals were more likely to exhibit unethical decision-making tendencies (study 3), take valued goods from others (study 4), lie in a negotiation (study 5), cheat to increase their chances of winning a prize (study 6), and endorse unethical behavior at work (study 7) than were lower-class individuals. Mediator and moderator data demonstrated that upper-class individuals’ unethical tendencies are accounted for, in part, by their more favorable attitudes toward greed.</blockquote><br />
See also [http://www.pnas.org/content/suppl/2012/02/22/1118373109.DCSupplemental/pnas.201118373SI.pdf "Supporting Information"], published online in Proceedings of the National Academy of Sciences of the USA, February 27, 2012.<br><br />
<br />
===Discussion===<br />
1. If you were going to write an article about this study, and you had access to the entire report, what would be the first, most basic, information you would want to provide to your readers about the “class” categories referred to in the abstract?<br><br />
2. The article indicates that the sample sizes for the first three experiments were “250,” “150 drivers,” and “105 students.” Besides the relatively small sample sizes, what other issues can you identify as a potential problems in making any inference about ethics from these experimental results? <br />
<br />
Submitted by Margaret Cibes<br />
<br />
==Judea Pearl wins Turing Prize==<br />
Danny Kaplan posted a link to this story on the Isolated Statisicians e-mail list:<br />
<br />
[http://bits.blogs.nytimes.com/2012/03/15/a-turing-award-for-helping-make-computers-smarter/?hpw A Turing Award for helping make computers smarter].<br><br />
by Steve Lohr, Bits blog, ''New York Times'', 15 March 2012<br />
<br />
Judea Pearl of UCLA has been awarded this year's Turing Prize by the Association for Computing Machinery. According to the article Pearl's work on probabilistic reasoning and Bayesian networks has influenced applications in areas from search engines to fraud detection to speech recognition. The article includes testimonials from many noted experts in the field of artificial intelligence.<br />
<br />
Danny's message provided links to [http://bayes.cs.ucla.edu/jp_home.html Pearl's web page] for references to his work on causality, and to this [http://singapore.cs.ucla.edu/LECTURE/lecture_sec1.htm talk], which is the epilogue to his famous book, [http://bayes.cs.ucla.edu/BOOK-99/book-toc.html ''Causality''].<br />
<br />
Danny's [http://www.mosaic-web.org/go/StatisticalModeling ''Statistical Modeling'' textbook] includes a [http://www.mosaic-web.org/go/StatisticalModeling/Chapters/Chapter-17.pdf chapter] which discusses some of these ideas at a level appropriate for an introductory statistics audience.<br />
<br />
==A bizarre anatomical correlation==<br />
<br />
[http://www.nytimes.com/2012/03/17/opinion/collins-politicians-swinging-stethoscopes.html Politicians Swinging Stethoscopes], Gail Collins, The New York Times, March 16, 2012.<br />
<br />
When a topic carries strong emotions, often people forget to check their facts carefully. And abortion is possibly the most emotional topic in politics today. It's not too surprising that opponents of abortion have tried to promote a link between abortion and breast cancer.<br />
<br />
<blockquote>New Hampshire, for instance, seems to have developed a thing for linking sex and malignant disease. This week, the State House passed a bill that required that women who want to terminate a pregnancy be informed that abortions were linked to "an increased risk of breast cancer." As Terie Norelli, the minority leader, put it, the Legislature is attempting to make it a felony for a doctor "to not give a patient inaccurate information."</blockquote><br />
<br />
This was actually an issue [http://en.wikipedia.org/wiki/C._Everett_Koop#Government_service about 25 years ago, when C. Everett Koop was Surgeon General]. The American Cancer Society (ACS) was written that [http://www.cancer.org/Cancer/BreastCancer/MoreInformation/is-abortion-linked-to-breast-cancer scientific research studies have not found a cause-and-effect relationship between abortion and breast cancer], and cites a comprehensive rview in 2003 by theh National Cancer Institute (NCI). But numerous pro-life sites still claim the opposite, with headlines like [http://www.lifenews.com/2010/09/21/nat-6718/ Hundreds of Studies Confirm Abortion-Breast Cancer Link].<br />
<br />
It is interesting to speculate why pro-life sites would promote the abortion/breast cancer link so strongly in spite of dismissive commentary from respected organizations like ACS and NCI. If you believe that abortion is murder (as many people do), then it is not too far a leap to believe that something this evil would necessarily carry bad health consequences at the same time. It may be a belief that mainstream organizations like ACS and NCI are dominated by [http://www.wnd.com/2010/01/121749/ pro-abortion extremists].<br />
<br />
The abortion/breast cancer link at least has biological plausibility, but another cancer link in an area almost as contentious lacks even this biological plausibility.<br />
<br />
<blockquote>And there’s more. One of the sponsors, Representative Jeanine Notter, recently asked a colleague whether he would be interested, "as a man," to know that there was a study "that links the pill to prostate cancer."</blockquote><br />
<br />
Clearly, Ms. Notter understands that only women consume birth control pills and that only men have a prostate. What she is claiming is <br />
<br />
<blockquote>that nations with high use of birth control pills among women also tended to have high rates of prostate cancer among men.</blockquote><br />
<br />
Gail Collins mocks this correlation.<br />
<br />
<blockquote>You could also possibly discover that nations with the lowest per capita number of ferrets have a higher rate of prostate cancer.</blockquote><br />
<br />
===Questions===<br />
<br />
1. What is the name for the type of study that notes that "nations with high use of birth control pills among women also tended to have high rates of prostate cancer among men"?<br />
<br />
2. Randomized studies of the link between abortion and breast cancer are clearly impossible. What types of observational studies might be used to examine this link. What are the strengths and weaknesses of those types of studies.</div>Simon66217https://www.causeweb.org/wiki/chance/index.php?title=Chance_News_83&diff=15447Chance News 832012-03-19T15:53:09Z<p>Simon66217: /* A bizarre anatomical correlation */</p>
<hr />
<div>==Quotations==<br />
“A poll is not laser surgery; it’s an estimate.”<br />
<div align=right>ABC News polling director in [http://abcnews.go.com/blogs/politics/2007/12/moe-and-mojo/ “MOE and Mojo”]<br><br />
ABC Blogs, December 3, 2007</div><br />
Submitted by Margaret Cibes<br />
<br />
----<br />
"The most famous result of Student’s experimental method is Student’s t-table. But the real end of Student’s inquiry was taste, quality control, and minimally efficient sample sizes for experimental Guinness – not to achieve statistical significance at the .05 level or, worse yet, boast about an artificially randomized experiment."<br />
<br />
<div align=right>--Stephen T. Ziliak, in [http://sites.roosevelt.edu/sziliak/files/2012/02/William-S-Gosset-and-Experimental-Statistics-Ziliak-JWE-2011.pdf W.S. Gosset and some neglected concepts in experimental statistics: Guinnessometrics II]</div><br />
<br />
(Ziliak is the co-author of [http://www.press.umich.edu/titleDetailPraise.do?id=186351 ''The Cult of Statistical Significance: How the Standard Error Costs Us Jobs, Justice, and Lives''])<br />
<br />
Submitted by Bill Peterson<br />
<br />
-----<br />
“[W. S. Gossett] wrote to R. A. Fisher of the t tables, "You are probably the only man who will ever use them (Box 1978)."<br><br />
<br />
“[W]e see the data analyst's insistence on ‘letting the data speak to us’ by plots and displays as an instinctive understanding of the need to encourage and to stimulate the pattern recognition and model generating capability of the right brain. Also, it expresses his concern that we not allow our pushy deductive left brain to take over too quickly and perhaps forcibly produce unwarranted conclusions based on an inadequate model.”<br />
<div align=right>George Box in “The Importance of Practice in the Development of Statistics”<br><br />
<i>Technometrics</i>, February 1984</div><br />
<br />
Thomas L. Moore recommended this article in an ISOSTAT posting. (It is available in [http://www.jstor.org/discover/10.2307/1268410?uid=3739952&uid=2129&uid=2134&uid=2&uid=70&uid=4&uid=3739256&sid=21100643590836 JSTOR].)<br />
<br />
----<br />
We are familiar with George Box’s famous statement: “All models are wrong but some are useful.” Here is another variant, cited in [http://en.wikiquote.org/wiki/George_E._P._Box Wikipedia]:<br />
:“Remember that all models are wrong; the practical question is how wrong do they have to be to not be useful.”<br />
<br />
Submitted by Margaret Cibes<br />
<br />
(Note. For an interesting discussion revisiting this theme, see [http://andrewgelman.com/2012/03/all-models-are-right-most-are-useless/ All models are right, most are useless] on Andrew Gelman's blog, 4 March 2012).<br />
----<br />
“There are two kinds of statistics: the kind you look up and the kind you make up.”<br />
<br />
<div align=right> --attributed to Rex Stout, American writer (1886 - 1975)</div><br />
Submitted by Paul Alper<br />
<br />
<br />
----<br />
<br />
“Definition of Statistics: The science of producing unreliable facts from reliable figures.”<br />
<br />
"The only science that enables different experts using the same figures to draw different conclusions."<br />
<br />
<div align=right> --attributed to Evan Esar, American humorist (1899–1995)</div><br />
Submitted by Paul Alper<br />
<br />
----<br />
“Science involves confronting our `absolute stupidity'. That kind of stupidity is an existential fact, inherent in our efforts to push our way into the unknown. …. Focusing on important questions puts us in the awkward position of being ignorant. One of the beautiful things about science is that it allows us to bumble along, getting it wrong time after time, and feel perfectly fine as long as we learn something each time. …. The more comfortable we become with being stupid, the deeper we will wade into the unknown and the more likely we are to make big discoveries.”<br />
<div align=right>UVa scientist Martin Schwartz in [http://jcs.biologists.org/content/121/11/1771.full “The importance of stupidity in scientific research”]<br><br />
<i>Journal of Cell Science</i>, 2008</div><br />
Submitted by Margaret Cibes<br />
<br />
==Forsooth==<br />
“In the first four months [at the new Resorts World Casino New York City], roughly 25,000 gamblers showed up every day, shoving a collective $2.3 billion through the slots and losing $140 million in the process. …. Resorts World offers more than 4,000 slot machines, but thanks to state law, there are no traditional card tables.”<br />
<div align=right>[http://online.wsj.com/article/SB10001424052970204792404577229481078006746.html?link=SM_clmst_sum#articleTabs%3Dcomments “The Gamblers’ New Game”]<br><br />
<i>The Wall Street Journal</i>, February 18, 2012</div><br />
Submitted by Margaret Cibes<br />
<br />
-----<br />
[[File:Elderly_drivers.jpg|400px]]<br><br><br />
“Drivers 85 and older still have a higher rate of deadly crashes than any other age group except teenagers.”<br><br />
<br />
(The article also describes two women who have learned to "compensate" for their macular degeneration in various ways - not necessarily welcome news!)<br />
<div align=right>[http://online.wsj.com/article/SB10001424052970204520204577251310179633818.html?KEYWORDS=sue+shellenbarger “Safer Over 70: Divers Keep the Keys”]<br><br />
<i>The Wall Street Journal</i>, February 29, 2012</div><br />
Submitted by Margaret Cibes<br />
<br />
-----<br />
[[File:PieChartMost.png|300px]]<br><br><br />
See the "Observation" at the top of the chart.<br />
<div align=right><br />
[http://junkcharts.typepad.com/junk_charts/2012/02/the-meaning-of-most.html “The Meaning of most”], downloaded from Junk Charts, March 1, 2012<br><br />
originally cited in [http://blog.kissmetrics.com/loading-time/?wide=1 “Mobile vs. Desktop”], KISSmetrics</div><br />
Submitted by Margaret Cibes<br />
<br />
-----<br />
<br />
“Here is the rub: Apple is so big, it’s running up against the law of large numbers. Also known as the golden theorem, with a proof attributed to the 17th-century Swiss mathematician Jacob Bernoulli, the law states that a variable will revert to a mean over a large sample of results. In the case of the largest companies, it suggests that high earnings growth and a rapid rise in share price will slow as those companies grow ever larger.”<br />
<div align=right>James Stewart in [http://www.nytimes.com/2012/02/25/business/apple-confronts-the-law-of-large-numbers-common-sense.html?_r=2&hp “Confronting a Law of Limits”]<br><br />
<i>The New York Times</i>, February 24, 2012</div><br />
Bill Peterson found Andrew Gelman’s comments[http://andrewgelman.com/2012/02/apple-confronts-the-law-of-large-numbers-huh/] about this article. <br />
<br />
Submitted by Margaret Cibes<br />
<br />
==Kaiser Fung on Minnesota’s ramp meters==<br />
<br />
A number references to Kaiser Fung’s book, ''Numbers Rule Your World'', appear in [http://test.causeweb.org/wiki/chance/index.php/Chance_News_82 Chance News 82]. From a Minnesotan’s point of view, however, the most important topic he discusses is not hurricanes, not drug testing, and not bias in standardized testing. Rather, the most critical issue is ramp metering as a means of improving traffic flow, relieving congestion and reducing travel time on Minnesota highways. “Industry experts regard Minnesota’s system of 430 ramp meters as a national model.”<br />
<br />
Unfortunately, “perception trumped reality.” An influential state senator, Dick Day, now a lobbyist for gambling interests, “led a charge to abolish the nationally recognized program, portraying it as part of the problem, not the solution.”<br />
<blockquote><br />
Leave it to Senator Day to speak the minds of “average Joes”--the people he meets at coffee shops, county fairs, summer parades, and the stock car races he loves. He saw ramp metering as a symbol of Big Government strangling our liberty.<br><br><br />
In the Twin Cities, drivers perceived their trip times to have lengthened [due to the ramp meters] even though in reality they have probably decreased. Thus, when in September 2000, the state legislature passed a mandate requiring MnDOT [Minnesota Department of Transportation] to conduct a “meters shutoff” experiment [of six weeks], the engineers [who devised the metering program] were stunned and disillusioned.<br />
</blockquote><br />
<br />
To make a long story short, when the ramp meters came back on, it turns out that: <br />
<blockquote><br />
[T]he engineering vision triumphed. Freeway conditions indeed worsened after the ramp meters went off. The key findings, based on actual measurements were as follows:<br />
*Peak freeway volume dropped by 9 percent.<br />
*Travel times rose by 22 percent, and the reliability deteriorated.<br />
*Travel speeds declined by 7 percent.<br />
*The number of crashes during merges jumped by 26 percent.<br />
</blockquote><br />
<br />
“The consultants further estimated that the benefits of ramp metering outweighed costs by five to one.” Nevertheless, the-above objective measures had to continue to battle subjective ones:<br />
<blockquote><br />
Despite the reality that commuters shortened their journeys if they waited their turns at the ramps, the drivers did not perceive the trade-off to be beneficial; they insisted that they would rather be moving slowly on the freeway than coming to a standstill at the ramp.<br />
</blockquote><br />
<br />
Accordingly, the engineers decided to modify the optimum solution to take into account driver psychology. “When they turned the lights back on, they limited waiting time on the ramps to four minutes, retired some unnecessary meters, and also shortened the operating hours.” Said differently, the constrained optimization model the engineers first considered left out some pivotal constraints.<br />
<br />
===Discussion===<br />
<br />
1. Do a search for “behavioral economics” to see the prevalence of irrational perceptions and subjective calculations in the economic sphere.<br />
<br />
2. Fung discusses an allied, albeit inverse, problem of waiting-time misconception. This instance concerns Disney World and its popular so-called FastPass as a means of avoiding queues. According to Fung<br />
<blockquote><br />
Clearly, FastPass users love the product--but how much waiting time can they save? Amazingly, the answer is none; they spend the same amount of time waiting for popular rides with or without FastPass!..So Disney confirms yet again that perception trumps reality. The FastPass concept is an absolute stroke of genius; it utterly changes perceived waiting times and has made many, many park-goers very, very giddy.<br />
</blockquote><br />
<br />
3. An oft-repeated and perhaps apocryphal operations research/statistics/decision theory anecdote has to do with elevators in a very large office building. Employees complained about excessive waiting times because the elevators all too frequently seemed to be in lockstep. Any physical solution such as creating a new elevator shaft or installing a complicated timing algorithm would be very expensive. The famous and utterly inexpensive psychological solution whereby perception trumped reality was to put in mirrors so that the waiting time would seem less because the employees would enjoy admiring themselves in the mirrors. Note that older and more benighted operations research/statistics/decision theory textbooks would have used the word “women” instead of “employees” in the previous sentence. <br />
<br />
4. A very modern and frustrating example of perception again trumping reality can often be observed in supermarkets which have installed self-checkout lanes without placing a limit on the number of items per shopper. In order to avoid a line at the regular checkout, some shoppers with an extremely large number of items will often choose the self-checkout and take much longer to finish than if had they queued at the regular checkout. Explain why said shoppers psychologically might prefer to persist in that behavior despite evidence to the contrary. Why don’t supermarkets simply limit the number of items per customer at self-checkout lanes?<br />
<br />
Submitted by Paul Alper<br />
<br />
==Don’t forget Chebyshev==<br />
<i>Super Crunchers</i>, by Ian Ayres, Random House, 2007<br><br />
<br />
<blockquote>When I taught at Stanford Law School, professors were required to award grades that had a 3.2 mean. …. The problem was that many of the students and many of the professors had no way to express the degree of variability in professors’ grading habits. …. As a nation, we lack a vocabulary of dispersion. We don’t know how to express what we intuitively know about the variability of a distribution of numbers. The 2SD [2 standard-deviation] rule could help give us this vocabulary. A professor who said that her standard deviation was .2 could have conveyed a lot of information with a single number. The problem is that very few people in the U.S. today understand what this means. But you should know and be able to explain to others that only about 2.5 percent of the professor’s grades are above 3.6. [pp. 221-222]</blockquote><br />
<br />
===Discussion===<br />
1. Suppose that a professor's <i>awarded</i> grades had mean 3.2 and SD 0.2.<br> <br />
(a) Under what condition could we say that “only about 2.5 percent of the professor’s grades are above 3.6”?<br><br />
(b) Without that condition, what could we say, if anything, about the percent of awarded grades <i>outside</i> of a 2SD range about the mean? About the percent of awarded grades <i>above</i> 3.6?<br><br />
2. Suppose that a professor's <i>raw grades</i> had mean 3.2 and SD 0.2. Do you think that this would be a realistic scenario in most undergraduate college classes? In most graduate-school classes? Why or why not?<br><br />
3. How could a professor construct a distribution of <i>awarded</i> grades with mean 3.2 and SD 0.2, based on <i>raw grades</i>, so that one could say that only about 2.5 percent of the <i>awarded</i> grades are above 3.6? What effect, if any, could that scaling have had on the worst – or on the best – raw grades?<br><br />
<br />
Submitted by Margaret Cibes<br />
<br />
==Critique of women-in-science statistics==<br />
[http://scholarship.claremont.edu/cgi/viewcontent.cgi?article=1041&context=jhm&sei-redir=1&referer=http%3A%2F%2Fwww.google.com%2Furl%3Fsa%3Dt%26rct%3Dj%26q%3Drumors%2520of%2520rarity%26source%3Dweb%26cd%3D1%26ved%3D0CCMQFjAA%26url%3Dhttp%253A%252F%252Fscholarship.claremont.edu%252Fcgi%252Fviewcontent.cgi%253Farticle%253D1041%2526context%253Djhm%26ei%3DugheT7arHMPV0QGzpfjkAw%26usg%3DAFQjCNGPJtXVTh10gldzpdqxaolp1Shd8Q#search=%22rumors%20rarity%22 “Rumors of Our Rarity are Greatly Exaggerated: Bad Statistics About Women in Science”]<br><br />
by Cathy Kessel, <i>Journal of Humanistic Mathematics</i>, July 2011<br><br />
<br />
Based on her apparently extensive and detailed study of reports about female-to-male ratios with respect to STEM abilities/careers, Kessel discusses three major problems with the statistics cited in them, as well as with the repetition of these questionable figures in subsequent academic and non-academic reports. <br />
<blockquote>Whatever their origins, statistics which are mislabeled, misinterpreted, fictitious, or otherwise defective remain in circulation because they are accepted by editors, readers, and referees.</blockquote><br />
<br />
“The Solitary Statistic.” A 13-to-1 boy-girl ratio in SAT-Math scores has been widely cited since it appeared in a 1983 <i>Science</i> article. That ratio was based on the scores of 280 seventh- and eighth-graders who scored 700 or above on the test over the period 1980-83. These students were part of a total of 64,000 students applying for a Johns Hopkins science program for exceptionally talented STEM-potential students. Kessel faults the widespread references to this outdated data, among other issues, and she cites more recent statistics at Hopkins and other such programs, including a ratio as low as 3 to 1 in 2005.<br><br />
<br />
“The Fabricated Statistic.” A “finding” that “Women talk almost three times as much as men” was published in <i>The Female Brain</i> in 2006. This was supposed to explain why women prefer careers which allow them to “connect and communicate” as opposed careers in science and engineering. Kessel outlines some issues that might make this explanation questionable.<br><br />
<br />
“The Garbled Statistic.” An example from “The Science of Sex Differences in Science and Mathematics,” published in <i>Psychological Science in the Public Interest</i> in 2007, was a report that women were “8.3% of tenure-track faculty at ‘elite’ mathematics departments.” A 2002 survey produced similar math data; that survey was based on the “top 50 departments.” These and other reports generally reported only the aggregate figure and not any of the raw data by rank. Kessel gives other examples in which raw data summary tables (which she had requested and received) would have been helpful to interpreting results.<br />
<blockquote>Although noticing mistakes may require numerical sophistication or knowledge of particular fields, accurate reporting of names, dates, and sources of<br />
statistics does not take much skill. At the very least, authors and research assistants can copy categories and sources as well as numbers. Editors can (and should) ask for sources.</blockquote><br />
===Discussion===<br />
1. Is there anything random about the group of students applying to a university’s program for talented students - or about the top SAT-M scorers in that group? Why are these important questions?<br><br />
2. Kessel quotes a statement that has been reported a number of times: “Women use 20,000 words per day, while men use 7,000." How do you think the researchers got these counts?<br><br />
3. Why might it be important to consider academic rank as a variable in analyzing the progress, or lack thereof, of women in obtaining university positions?<br><br />
4. Why might it be important to know more about the sponsorship of these studies – researcher affiliations, funding, <i>etc.</i>?<br><br />
<br />
Submitted by Margaret Cibes, based on a reference in March 2012 <i>College Mathematics Journal</i><br />
<br />
==Ethics study of social classes==<br />
[http://blogs.wsj.com/ideas-market/2012/02/27/study-high-social-class-predicts-unethical-behavior/tab/print/ “Study: High Social Class Predicts Unethical Behavior”]<br><br />
<i>The Wall Street Journal</i>, February 27, 2012<br><br />
<br />
Here is an abstract of the study[http://www.pnas.org/content/early/2012/02/21/1118373109.abstract] referred to in the article:<br />
<blockquote>Seven studies using experimental and naturalistic methods reveal that upper-class individuals behave more unethically than lower-class individuals. In studies 1 and 2, upper-class individuals were more likely to break the law while driving, relative to lower-class individuals. In follow-up laboratory studies, upper-class individuals were more likely to exhibit unethical decision-making tendencies (study 3), take valued goods from others (study 4), lie in a negotiation (study 5), cheat to increase their chances of winning a prize (study 6), and endorse unethical behavior at work (study 7) than were lower-class individuals. Mediator and moderator data demonstrated that upper-class individuals’ unethical tendencies are accounted for, in part, by their more favorable attitudes toward greed.</blockquote><br />
See also [http://www.pnas.org/content/suppl/2012/02/22/1118373109.DCSupplemental/pnas.201118373SI.pdf "Supporting Information"], published online in Proceedings of the National Academy of Sciences of the USA, February 27, 2012.<br><br />
<br />
===Discussion===<br />
1. If you were going to write an article about this study, and you had access to the entire report, what would be the first, most basic, information you would want to provide to your readers about the “class” categories referred to in the abstract?<br><br />
2. The article indicates that the sample sizes for the first three experiments were “250,” “150 drivers,” and “105 students.” Besides the relatively small sample sizes, what other issues can you identify as a potential problems in making any inference about ethics from these experimental results? <br />
<br />
Submitted by Margaret Cibes<br />
<br />
==Judea Pearl wins Turing Prize==<br />
Danny Kaplan posted a link to this story on the Isolated Statisicians e-mail list:<br />
<br />
[http://bits.blogs.nytimes.com/2012/03/15/a-turing-award-for-helping-make-computers-smarter/?hpw A Turing Award for helping make computers smarter].<br><br />
by Steve Lohr, Bits blog, ''New York Times'', 15 March 2012<br />
<br />
Judea Pearl of UCLA has been awarded this year's Turing Prize by the Association for Computing Machinery. According to the article Pearl's work on probabilistic reasoning and Bayesian networks has influenced applications in areas from search engines to fraud detection to speech recognition. The article includes testimonials from many noted experts in the field of artificial intelligence.<br />
<br />
Danny's message provided links to [http://bayes.cs.ucla.edu/jp_home.html Pearl's web page] for references to his work on causality, and to this [http://singapore.cs.ucla.edu/LECTURE/lecture_sec1.htm talk], which is the epilogue to his famous book, [http://bayes.cs.ucla.edu/BOOK-99/book-toc.html ''Causality''].<br />
<br />
Danny's [http://www.mosaic-web.org/go/StatisticalModeling ''Statistical Modeling'' textbook] includes a [http://www.mosaic-web.org/go/StatisticalModeling/Chapters/Chapter-17.pdf chapter] which discusses some of these ideas at a level appropriate for an introductory statistics audience.<br />
<br />
==A bizarre anatomical correlation==<br />
<br />
[http://www.nytimes.com/2012/03/17/opinion/collins-politicians-swinging-stethoscopes.html Politicians Swinging Stethoscopes], Gail Collins, The New York Times, March 16, 2012.<br />
<br />
When a topic carries strong emotions, often people forget to check their facts carefully. And abortion is possibly the most emotional topic in politics today. It's not too surprising that opponents of abortion have tried to promote a link between abortion and breast cancer.<br />
<br />
<blockquote>New Hampshire, for instance, seems to have developed a thing for linking sex and malignant disease. This week, the State House passed a bill that required that women who want to terminate a pregnancy be informed that abortions were linked to "an increased risk of breast cancer." As Terie Norelli, the minority leader, put it, the Legislature is attempting to make it a felony for a doctor "to not give a patient inaccurate information."</blockquote><br />
<br />
This was actually an issue [http://en.wikipedia.org/wiki/C._Everett_Koop#Government_service about 25 years ago, when C. Everett Koop was Surgeon General]. The American Cancer Society was written that [http://www.cancer.org/Cancer/BreastCancer/MoreInformation/is-abortion-linked-to-breast-cancer scientific research studies have not found a cause-and-effect relationship between abortion and breast cancer] but numerous anti-abortion sites still claim the opposite, with headlines like [http://www.lifenews.com/2010/09/21/nat-6718/ Hundreds of Studies Confirm Abortion-Breast Cancer Link] .</div>Simon66217https://www.causeweb.org/wiki/chance/index.php?title=Chance_News_83&diff=15446Chance News 832012-03-19T15:39:28Z<p>Simon66217: /* Judea Pearl wins Turing Prize */</p>
<hr />
<div>==Quotations==<br />
“A poll is not laser surgery; it’s an estimate.”<br />
<div align=right>ABC News polling director in [http://abcnews.go.com/blogs/politics/2007/12/moe-and-mojo/ “MOE and Mojo”]<br><br />
ABC Blogs, December 3, 2007</div><br />
Submitted by Margaret Cibes<br />
<br />
----<br />
"The most famous result of Student’s experimental method is Student’s t-table. But the real end of Student’s inquiry was taste, quality control, and minimally efficient sample sizes for experimental Guinness – not to achieve statistical significance at the .05 level or, worse yet, boast about an artificially randomized experiment."<br />
<br />
<div align=right>--Stephen T. Ziliak, in [http://sites.roosevelt.edu/sziliak/files/2012/02/William-S-Gosset-and-Experimental-Statistics-Ziliak-JWE-2011.pdf W.S. Gosset and some neglected concepts in experimental statistics: Guinnessometrics II]</div><br />
<br />
(Ziliak is the co-author of [http://www.press.umich.edu/titleDetailPraise.do?id=186351 ''The Cult of Statistical Significance: How the Standard Error Costs Us Jobs, Justice, and Lives''])<br />
<br />
Submitted by Bill Peterson<br />
<br />
-----<br />
“[W. S. Gossett] wrote to R. A. Fisher of the t tables, "You are probably the only man who will ever use them (Box 1978)."<br><br />
<br />
“[W]e see the data analyst's insistence on ‘letting the data speak to us’ by plots and displays as an instinctive understanding of the need to encourage and to stimulate the pattern recognition and model generating capability of the right brain. Also, it expresses his concern that we not allow our pushy deductive left brain to take over too quickly and perhaps forcibly produce unwarranted conclusions based on an inadequate model.”<br />
<div align=right>George Box in “The Importance of Practice in the Development of Statistics”<br><br />
<i>Technometrics</i>, February 1984</div><br />
<br />
Thomas L. Moore recommended this article in an ISOSTAT posting. (It is available in [http://www.jstor.org/discover/10.2307/1268410?uid=3739952&uid=2129&uid=2134&uid=2&uid=70&uid=4&uid=3739256&sid=21100643590836 JSTOR].)<br />
<br />
----<br />
We are familiar with George Box’s famous statement: “All models are wrong but some are useful.” Here is another variant, cited in [http://en.wikiquote.org/wiki/George_E._P._Box Wikipedia]:<br />
:“Remember that all models are wrong; the practical question is how wrong do they have to be to not be useful.”<br />
<br />
Submitted by Margaret Cibes<br />
<br />
(Note. For an interesting discussion revisiting this theme, see [http://andrewgelman.com/2012/03/all-models-are-right-most-are-useless/ All models are right, most are useless] on Andrew Gelman's blog, 4 March 2012).<br />
----<br />
“There are two kinds of statistics: the kind you look up and the kind you make up.”<br />
<br />
<div align=right> --attributed to Rex Stout, American writer (1886 - 1975)</div><br />
Submitted by Paul Alper<br />
<br />
<br />
----<br />
<br />
“Definition of Statistics: The science of producing unreliable facts from reliable figures.”<br />
<br />
"The only science that enables different experts using the same figures to draw different conclusions."<br />
<br />
<div align=right> --attributed to Evan Esar, American humorist (1899–1995)</div><br />
Submitted by Paul Alper<br />
<br />
----<br />
“Science involves confronting our `absolute stupidity'. That kind of stupidity is an existential fact, inherent in our efforts to push our way into the unknown. …. Focusing on important questions puts us in the awkward position of being ignorant. One of the beautiful things about science is that it allows us to bumble along, getting it wrong time after time, and feel perfectly fine as long as we learn something each time. …. The more comfortable we become with being stupid, the deeper we will wade into the unknown and the more likely we are to make big discoveries.”<br />
<div align=right>UVa scientist Martin Schwartz in [http://jcs.biologists.org/content/121/11/1771.full “The importance of stupidity in scientific research”]<br><br />
<i>Journal of Cell Science</i>, 2008</div><br />
Submitted by Margaret Cibes<br />
<br />
==Forsooth==<br />
“In the first four months [at the new Resorts World Casino New York City], roughly 25,000 gamblers showed up every day, shoving a collective $2.3 billion through the slots and losing $140 million in the process. …. Resorts World offers more than 4,000 slot machines, but thanks to state law, there are no traditional card tables.”<br />
<div align=right>[http://online.wsj.com/article/SB10001424052970204792404577229481078006746.html?link=SM_clmst_sum#articleTabs%3Dcomments “The Gamblers’ New Game”]<br><br />
<i>The Wall Street Journal</i>, February 18, 2012</div><br />
Submitted by Margaret Cibes<br />
<br />
-----<br />
[[File:Elderly_drivers.jpg|400px]]<br><br><br />
“Drivers 85 and older still have a higher rate of deadly crashes than any other age group except teenagers.”<br><br />
<br />
(The article also describes two women who have learned to "compensate" for their macular degeneration in various ways - not necessarily welcome news!)<br />
<div align=right>[http://online.wsj.com/article/SB10001424052970204520204577251310179633818.html?KEYWORDS=sue+shellenbarger “Safer Over 70: Divers Keep the Keys”]<br><br />
<i>The Wall Street Journal</i>, February 29, 2012</div><br />
Submitted by Margaret Cibes<br />
<br />
-----<br />
[[File:PieChartMost.png|300px]]<br><br><br />
See the "Observation" at the top of the chart.<br />
<div align=right><br />
[http://junkcharts.typepad.com/junk_charts/2012/02/the-meaning-of-most.html “The Meaning of most”], downloaded from Junk Charts, March 1, 2012<br><br />
originally cited in [http://blog.kissmetrics.com/loading-time/?wide=1 “Mobile vs. Desktop”], KISSmetrics</div><br />
Submitted by Margaret Cibes<br />
<br />
-----<br />
<br />
“Here is the rub: Apple is so big, it’s running up against the law of large numbers. Also known as the golden theorem, with a proof attributed to the 17th-century Swiss mathematician Jacob Bernoulli, the law states that a variable will revert to a mean over a large sample of results. In the case of the largest companies, it suggests that high earnings growth and a rapid rise in share price will slow as those companies grow ever larger.”<br />
<div align=right>James Stewart in [http://www.nytimes.com/2012/02/25/business/apple-confronts-the-law-of-large-numbers-common-sense.html?_r=2&hp “Confronting a Law of Limits”]<br><br />
<i>The New York Times</i>, February 24, 2012</div><br />
Bill Peterson found Andrew Gelman’s comments[http://andrewgelman.com/2012/02/apple-confronts-the-law-of-large-numbers-huh/] about this article. <br />
<br />
Submitted by Margaret Cibes<br />
<br />
==Kaiser Fung on Minnesota’s ramp meters==<br />
<br />
A number references to Kaiser Fung’s book, ''Numbers Rule Your World'', appear in [http://test.causeweb.org/wiki/chance/index.php/Chance_News_82 Chance News 82]. From a Minnesotan’s point of view, however, the most important topic he discusses is not hurricanes, not drug testing, and not bias in standardized testing. Rather, the most critical issue is ramp metering as a means of improving traffic flow, relieving congestion and reducing travel time on Minnesota highways. “Industry experts regard Minnesota’s system of 430 ramp meters as a national model.”<br />
<br />
Unfortunately, “perception trumped reality.” An influential state senator, Dick Day, now a lobbyist for gambling interests, “led a charge to abolish the nationally recognized program, portraying it as part of the problem, not the solution.”<br />
<blockquote><br />
Leave it to Senator Day to speak the minds of “average Joes”--the people he meets at coffee shops, county fairs, summer parades, and the stock car races he loves. He saw ramp metering as a symbol of Big Government strangling our liberty.<br><br><br />
In the Twin Cities, drivers perceived their trip times to have lengthened [due to the ramp meters] even though in reality they have probably decreased. Thus, when in September 2000, the state legislature passed a mandate requiring MnDOT [Minnesota Department of Transportation] to conduct a “meters shutoff” experiment [of six weeks], the engineers [who devised the metering program] were stunned and disillusioned.<br />
</blockquote><br />
<br />
To make a long story short, when the ramp meters came back on, it turns out that: <br />
<blockquote><br />
[T]he engineering vision triumphed. Freeway conditions indeed worsened after the ramp meters went off. The key findings, based on actual measurements were as follows:<br />
*Peak freeway volume dropped by 9 percent.<br />
*Travel times rose by 22 percent, and the reliability deteriorated.<br />
*Travel speeds declined by 7 percent.<br />
*The number of crashes during merges jumped by 26 percent.<br />
</blockquote><br />
<br />
“The consultants further estimated that the benefits of ramp metering outweighed costs by five to one.” Nevertheless, the-above objective measures had to continue to battle subjective ones:<br />
<blockquote><br />
Despite the reality that commuters shortened their journeys if they waited their turns at the ramps, the drivers did not perceive the trade-off to be beneficial; they insisted that they would rather be moving slowly on the freeway than coming to a standstill at the ramp.<br />
</blockquote><br />
<br />
Accordingly, the engineers decided to modify the optimum solution to take into account driver psychology. “When they turned the lights back on, they limited waiting time on the ramps to four minutes, retired some unnecessary meters, and also shortened the operating hours.” Said differently, the constrained optimization model the engineers first considered left out some pivotal constraints.<br />
<br />
===Discussion===<br />
<br />
1. Do a search for “behavioral economics” to see the prevalence of irrational perceptions and subjective calculations in the economic sphere.<br />
<br />
2. Fung discusses an allied, albeit inverse, problem of waiting-time misconception. This instance concerns Disney World and its popular so-called FastPass as a means of avoiding queues. According to Fung<br />
<blockquote><br />
Clearly, FastPass users love the product--but how much waiting time can they save? Amazingly, the answer is none; they spend the same amount of time waiting for popular rides with or without FastPass!..So Disney confirms yet again that perception trumps reality. The FastPass concept is an absolute stroke of genius; it utterly changes perceived waiting times and has made many, many park-goers very, very giddy.<br />
</blockquote><br />
<br />
3. An oft-repeated and perhaps apocryphal operations research/statistics/decision theory anecdote has to do with elevators in a very large office building. Employees complained about excessive waiting times because the elevators all too frequently seemed to be in lockstep. Any physical solution such as creating a new elevator shaft or installing a complicated timing algorithm would be very expensive. The famous and utterly inexpensive psychological solution whereby perception trumped reality was to put in mirrors so that the waiting time would seem less because the employees would enjoy admiring themselves in the mirrors. Note that older and more benighted operations research/statistics/decision theory textbooks would have used the word “women” instead of “employees” in the previous sentence. <br />
<br />
4. A very modern and frustrating example of perception again trumping reality can often be observed in supermarkets which have installed self-checkout lanes without placing a limit on the number of items per shopper. In order to avoid a line at the regular checkout, some shoppers with an extremely large number of items will often choose the self-checkout and take much longer to finish than if had they queued at the regular checkout. Explain why said shoppers psychologically might prefer to persist in that behavior despite evidence to the contrary. Why don’t supermarkets simply limit the number of items per customer at self-checkout lanes?<br />
<br />
Submitted by Paul Alper<br />
<br />
==Don’t forget Chebyshev==<br />
<i>Super Crunchers</i>, by Ian Ayres, Random House, 2007<br><br />
<br />
<blockquote>When I taught at Stanford Law School, professors were required to award grades that had a 3.2 mean. …. The problem was that many of the students and many of the professors had no way to express the degree of variability in professors’ grading habits. …. As a nation, we lack a vocabulary of dispersion. We don’t know how to express what we intuitively know about the variability of a distribution of numbers. The 2SD [2 standard-deviation] rule could help give us this vocabulary. A professor who said that her standard deviation was .2 could have conveyed a lot of information with a single number. The problem is that very few people in the U.S. today understand what this means. But you should know and be able to explain to others that only about 2.5 percent of the professor’s grades are above 3.6. [pp. 221-222]</blockquote><br />
<br />
===Discussion===<br />
1. Suppose that a professor's <i>awarded</i> grades had mean 3.2 and SD 0.2.<br> <br />
(a) Under what condition could we say that “only about 2.5 percent of the professor’s grades are above 3.6”?<br><br />
(b) Without that condition, what could we say, if anything, about the percent of awarded grades <i>outside</i> of a 2SD range about the mean? About the percent of awarded grades <i>above</i> 3.6?<br><br />
2. Suppose that a professor's <i>raw grades</i> had mean 3.2 and SD 0.2. Do you think that this would be a realistic scenario in most undergraduate college classes? In most graduate-school classes? Why or why not?<br><br />
3. How could a professor construct a distribution of <i>awarded</i> grades with mean 3.2 and SD 0.2, based on <i>raw grades</i>, so that one could say that only about 2.5 percent of the <i>awarded</i> grades are above 3.6? What effect, if any, could that scaling have had on the worst – or on the best – raw grades?<br><br />
<br />
Submitted by Margaret Cibes<br />
<br />
==Critique of women-in-science statistics==<br />
[http://scholarship.claremont.edu/cgi/viewcontent.cgi?article=1041&context=jhm&sei-redir=1&referer=http%3A%2F%2Fwww.google.com%2Furl%3Fsa%3Dt%26rct%3Dj%26q%3Drumors%2520of%2520rarity%26source%3Dweb%26cd%3D1%26ved%3D0CCMQFjAA%26url%3Dhttp%253A%252F%252Fscholarship.claremont.edu%252Fcgi%252Fviewcontent.cgi%253Farticle%253D1041%2526context%253Djhm%26ei%3DugheT7arHMPV0QGzpfjkAw%26usg%3DAFQjCNGPJtXVTh10gldzpdqxaolp1Shd8Q#search=%22rumors%20rarity%22 “Rumors of Our Rarity are Greatly Exaggerated: Bad Statistics About Women in Science”]<br><br />
by Cathy Kessel, <i>Journal of Humanistic Mathematics</i>, July 2011<br><br />
<br />
Based on her apparently extensive and detailed study of reports about female-to-male ratios with respect to STEM abilities/careers, Kessel discusses three major problems with the statistics cited in them, as well as with the repetition of these questionable figures in subsequent academic and non-academic reports. <br />
<blockquote>Whatever their origins, statistics which are mislabeled, misinterpreted, fictitious, or otherwise defective remain in circulation because they are accepted by editors, readers, and referees.</blockquote><br />
<br />
“The Solitary Statistic.” A 13-to-1 boy-girl ratio in SAT-Math scores has been widely cited since it appeared in a 1983 <i>Science</i> article. That ratio was based on the scores of 280 seventh- and eighth-graders who scored 700 or above on the test over the period 1980-83. These students were part of a total of 64,000 students applying for a Johns Hopkins science program for exceptionally talented STEM-potential students. Kessel faults the widespread references to this outdated data, among other issues, and she cites more recent statistics at Hopkins and other such programs, including a ratio as low as 3 to 1 in 2005.<br><br />
<br />
“The Fabricated Statistic.” A “finding” that “Women talk almost three times as much as men” was published in <i>The Female Brain</i> in 2006. This was supposed to explain why women prefer careers which allow them to “connect and communicate” as opposed careers in science and engineering. Kessel outlines some issues that might make this explanation questionable.<br><br />
<br />
“The Garbled Statistic.” An example from “The Science of Sex Differences in Science and Mathematics,” published in <i>Psychological Science in the Public Interest</i> in 2007, was a report that women were “8.3% of tenure-track faculty at ‘elite’ mathematics departments.” A 2002 survey produced similar math data; that survey was based on the “top 50 departments.” These and other reports generally reported only the aggregate figure and not any of the raw data by rank. Kessel gives other examples in which raw data summary tables (which she had requested and received) would have been helpful to interpreting results.<br />
<blockquote>Although noticing mistakes may require numerical sophistication or knowledge of particular fields, accurate reporting of names, dates, and sources of<br />
statistics does not take much skill. At the very least, authors and research assistants can copy categories and sources as well as numbers. Editors can (and should) ask for sources.</blockquote><br />
===Discussion===<br />
1. Is there anything random about the group of students applying to a university’s program for talented students - or about the top SAT-M scorers in that group? Why are these important questions?<br><br />
2. Kessel quotes a statement that has been reported a number of times: “Women use 20,000 words per day, while men use 7,000." How do you think the researchers got these counts?<br><br />
3. Why might it be important to consider academic rank as a variable in analyzing the progress, or lack thereof, of women in obtaining university positions?<br><br />
4. Why might it be important to know more about the sponsorship of these studies – researcher affiliations, funding, <i>etc.</i>?<br><br />
<br />
Submitted by Margaret Cibes, based on a reference in March 2012 <i>College Mathematics Journal</i><br />
<br />
==Ethics study of social classes==<br />
[http://blogs.wsj.com/ideas-market/2012/02/27/study-high-social-class-predicts-unethical-behavior/tab/print/ “Study: High Social Class Predicts Unethical Behavior”]<br><br />
<i>The Wall Street Journal</i>, February 27, 2012<br><br />
<br />
Here is an abstract of the study[http://www.pnas.org/content/early/2012/02/21/1118373109.abstract] referred to in the article:<br />
<blockquote>Seven studies using experimental and naturalistic methods reveal that upper-class individuals behave more unethically than lower-class individuals. In studies 1 and 2, upper-class individuals were more likely to break the law while driving, relative to lower-class individuals. In follow-up laboratory studies, upper-class individuals were more likely to exhibit unethical decision-making tendencies (study 3), take valued goods from others (study 4), lie in a negotiation (study 5), cheat to increase their chances of winning a prize (study 6), and endorse unethical behavior at work (study 7) than were lower-class individuals. Mediator and moderator data demonstrated that upper-class individuals’ unethical tendencies are accounted for, in part, by their more favorable attitudes toward greed.</blockquote><br />
See also [http://www.pnas.org/content/suppl/2012/02/22/1118373109.DCSupplemental/pnas.201118373SI.pdf "Supporting Information"], published online in Proceedings of the National Academy of Sciences of the USA, February 27, 2012.<br><br />
<br />
===Discussion===<br />
1. If you were going to write an article about this study, and you had access to the entire report, what would be the first, most basic, information you would want to provide to your readers about the “class” categories referred to in the abstract?<br><br />
2. The article indicates that the sample sizes for the first three experiments were “250,” “150 drivers,” and “105 students.” Besides the relatively small sample sizes, what other issues can you identify as a potential problems in making any inference about ethics from these experimental results? <br />
<br />
Submitted by Margaret Cibes<br />
<br />
==Judea Pearl wins Turing Prize==<br />
Danny Kaplan posted a link to this story on the Isolated Statisicians e-mail list:<br />
<br />
[http://bits.blogs.nytimes.com/2012/03/15/a-turing-award-for-helping-make-computers-smarter/?hpw A Turing Award for helping make computers smarter].<br><br />
by Steve Lohr, Bits blog, ''New York Times'', 15 March 2012<br />
<br />
Judea Pearl of UCLA has been awarded this year's Turing Prize by the Association for Computing Machinery. According to the article Pearl's work on probabilistic reasoning and Bayesian networks has influenced applications in areas from search engines to fraud detection to speech recognition. The article includes testimonials from many noted experts in the field of artificial intelligence.<br />
<br />
Danny's message provided links to [http://bayes.cs.ucla.edu/jp_home.html Pearl's web page] for references to his work on causality, and to this [http://singapore.cs.ucla.edu/LECTURE/lecture_sec1.htm talk], which is the epilogue to his famous book, [http://bayes.cs.ucla.edu/BOOK-99/book-toc.html ''Causality''].<br />
<br />
Danny's [http://www.mosaic-web.org/go/StatisticalModeling ''Statistical Modeling'' textbook] includes a [http://www.mosaic-web.org/go/StatisticalModeling/Chapters/Chapter-17.pdf chapter] which discusses some of these ideas at a level appropriate for an introductory statistics audience.<br />
<br />
==A bizarre anatomical correlation==<br />
<br />
[http://www.nytimes.com/2012/03/17/opinion/collins-politicians-swinging-stethoscopes.html Politicians Swinging Stethoscopes], Gail Collins, The New York Times, March 16, 2012.<br />
<br />
When a topic carries strong emotions, often people forget to check their facts carefully. And abortion is possibly the most emotional topic in politics today. It's not too surprising that opponents of abortion have tried to promote a link between abortion and breast cancer.<br />
<br />
<blockquote>New Hampshire, for instance, seems to have developed a thing for linking sex and malignant disease. This week, the State House passed a bill that required that women who want to terminate a pregnancy be informed that abortions were linked to "an increased risk of breast cancer." As Terie Norelli, the minority leader, put it, the Legislature is attempting to make it a felony for a doctor "to not give a patient inaccurate information."</blockquote><br />
<br />
This was actually an issue [http://en.wikipedia.org/wiki/C._Everett_Koop#Government_service about 25 years ago, when C. Everett Koop was Surgeon General].</div>Simon66217https://www.causeweb.org/wiki/chance/index.php?title=Chance_News_82&diff=15188Chance News 822012-02-15T22:24:28Z<p>Simon66217: /* Flood of data means flood of job opportunities */</p>
<hr />
<div>==Quotations==<br />
"I focus on the most important form of innumeracy in everyday life, statistical innumeracy--that is, the inability to reason about uncertainties and risk."<br />
<div align=right>--Gerd Gigerenzer</div><br />
<br />
Submitted by Bill Peterson<br />
<br />
==Forsooth==<br />
“[The] ballad ‘Someone Like you’ … has risen to near-iconic status recently, due in large part to its uncanny power to elicit tears and chills from listeners. …. Last year, [scientists] at McGill University reported that emotionally intense music releases dopamine in the pleasure and reward centers of the brain, similar to the effects of food, sex and drugs. …. Measuring listeners' responses, [the] team found that the <b><i>number of goose bumps observed</i></b> correlated with the amount of dopamine released, even when the music was extremely sad.”<br />
<div align=right>[http://online.wsj.com/article/SB10001424052970203646004577213010291701378.html?KEYWORDS=anatomy+of+tear-jerker “Anatomy of a Tear-Jerker”] (italics added)<br><br />
<i>The Wall Street Journal</i>, February 11, 2012</div><br />
Submitted by Margaret Cibes<br />
<br />
==Predictioneering==<br />
<br />
Bruce Bueno de Mesquita has written a fascinating, readable book, [http://www.predictioneersgame.com/ ''The Predictioneer’s Game: Using the Logic of Brazen Self-Interest to See and Shape the Future'']. A lengthy and generally positive review of Bueno de Mesquita’s views may be found in a NYT article, [http://www.nytimes.com/2009/08/16/magazine/16Bruce-t.html?_r=1&hpw=&pagewanted=all Can game theory predict when Iran will get the bomb?], by Clive Thompson (12 August 2009).<br />
<br />
His game-theory-based track record is indicated by:<br />
<blockquote><br />
For 29 years, Bueno de Mesquita has been developing and honing a computer model that predicts the outcome of any situation in which parties can be described as trying to persuade or coerce one another. Since the early 1980s, C.I.A. officials have hired him to perform more than a thousand predictions; a study by the C.I.A., now declassified, found that Bueno de Mesquita’s predictions “hit the bull’s-eye” twice as often as its own analysts did.<br />
</blockquote><br />
<br />
In the introduction to his book, Bueno de Mesquita says, “I have been predicting future events for three decades, often in print before the fact, and mostly getting them right.” Furthermore, “In my experience, government and private business want firm answers. They get plenty of wishy-washy predictions from their staff. They are looking for more than ‘On the one hand this, but on the other hand that’--and I give it to them.”<br />
<br />
===Discussion===<br />
<ol><br />
<li> In that NYT article may be found a statement shocking to the world of statistics and probability:<br />
<blockquote>Bueno de Mesquita does not express his forecasts in probabilistic terms; he says an event will transpire or it won’t. </blockquote><br />
Why is this a shocking statement to statisticians and probabilists?<br />
<br />
<li>In the NYT article is found the following criticism by Stephen Walt, a professor of international affairs at Harvard: <br />
<blockquote><br />
While Bueno de Mesquita has published many predictions in academic journals, the vast majority of his forecasts have been done in secret for corporate or government clients, where no independent academics can verify them. “We have no idea if he’s right 9 times out of 10, or 9 times out of a hundred, or 9 times out of a thousand,” Walt says. Walt also isn’t impressed by Stanley Feder’s C.I.A. study showing Bueno de Mesquita’s 90 percent hit rate. “It’s one midlevel C.I.A. bureaucrat saying, ‘This was a useful tool,’ ” Walt says.</blockquote><br />
Along these lines, suppose someone avers his hit rate is 100% when it involves forecasting a male birth, that is Prob (male predicted|male) = 1. Why might this be less than impressive?<br />
<br />
<li>Another critic may be found [http://decision-making.moshe-online.com/criticism_of_bueno_de_mesquita.html here] regarding a prediction about Libya. <blockquote><br />
In February 2011 Bueno de Mesquita predicted that the unrest in the Arab world will not spread to such places as Saudia Arabia and ... Libya. Yes, Libya. Watch and listen carefully to the segment starting at 1:51 min into the interview. <br />
</blockquote><br />
Other incorrect predictions made by Bueno de Mesquita are also noted on this web site, including what this author calls “The n factorial debacle” whereby Bueno de Mesquita misconstrues the number of possible interactions between n individuals (game participants). This web site also brings up the issue of the so-called “black swans” when it comes to predicting outcomes of the game. What is a black swan and why does a black swan have an impact on prediction?<br />
<br />
<li> Brazen Self-Interest and its mathematical logic rest on game theory which asserts that morality or any other nicety is counter productive to achieving success. Bueno de Mesquita’s particular computer model starts with data of expert opinion and then somehow via simulation iterates to a conclusion. Comment on the problem of local minimums/maximums.<br />
<br />
<li>Health care is in the news today as it was back in the 1990s. The NYT article notes that “In early 1993, a corporate client asked him to forecast whether the Clinton administration’s health care plan would pass, and he said it would.” The black swan in this instance was Congressman Daniel Rostenkowski who [page 125] “was the key to getting health care legislation through Congress.” Google Daniel Rostenkowski to see why Rostenkowski was a black swan and “contrary to my expectations, nothing passed through Congress.”<br />
</ol><br />
<br />
Submitted by Paul Alper<br />
<br />
==Flood of data means flood of job opportunities==<br />
<br />
[http://www.nytimes.com/2012/02/12/sunday-review/big-datas-impact-in-the-world.html The Age of Big Data], Steve Lohr, The New York Times, February 11, 2012.<br />
<br />
If you like working with data, you have great career opportunities ahead of you. We are seeing an<br />
<br />
<blockquote>an explosion of data, Web traffic and social network comments, as well as software and sensors that monitor shipments, suppliers and customer</blockquote><br />
<br />
This means a big deal for the job market.<br />
<br />
<blockquote>A report last year by the McKinsey Global Institute, the research arm of the consulting firm, projected that the United States needs 140,000 to 190,000 more workers with “deep analytical” expertise and 1.5 million more data-literate managers, whether retrained or hired.</blockquote><br />
<br />
It is a trend that occurs in more than business. This article cites major changes in Political Science and Public Health. The article introduces a term "big data" which it defines as<br />
<br />
<blockquote>shorthand for advancing trends in technology that open the door to a new approach to understanding the world and making decisions.</blockquote><br />
<br />
While the article extols the virtues of data analysis, for the most part, there are some cautionary statements.<br />
<br />
<blockquote>Big Data has its perils, to be sure. With huge data sets and fine-grained measurement, statisticians and computer scientists note, there is increased risk of “false discoveries.” The trouble with seeking a meaningful needle in massive haystacks of data, says Trevor Hastie, a statistics professor at Stanford, is that 'many bits of straw look like needles.' </blockquote><br />
<br />
Now data analysis demanding more attention from business circles and more.<br />
<br />
<blockquote>Veteran data analysts tell of friends who were long bored by discussions of their work but now are suddenly curious. “Moneyball” helped, they say, but things have gone way beyond that. “The culture has changed,” says Andrew Gelman, a statistician and political scientist at Columbia University. “There is this idea that numbers and statistics are interesting and fun. It’s cool now.”</blockquote><br />
<br />
Submitted by Steve Simon<br />
<br />
==Martin Gardner's "mistake"==<br />
[http://docserver.ingentaconnect.com/deliver/connect/maa/07468342/v43n1/s6.pdf?expires=1329338290&id=67229128&titleid=75000908&accname=Guest+User&checksum=00F4070339B8BAA7FC302598A2063EA7 “Martin Gardner’s Mistake”]<br><br />
by Tanya Khovanova, <i>The College Mathematics Journal</i>, January 2012<br><br />
<br />
Martin Gardner first discussed the following problem in 1959:<br />
<blockquote>Mr. Smith has two children. At least one of them is a boy. What is the probability that both children are boys?</blockquote><br />
His answer at that time follows:<br />
<blockquote>If Smith has two children, at least one of which is a boy, we have three equally probable cases: boy-boy, boy-girl, girl-boy. In only one case are both children boys, so the probability that both are boys is 1/3.</blockquote><br />
Gardner later wrote a "correction" to his original solution, indicating that “the answer depends on the procedure by which the information is ‘at least one is a boy’ is obtained.” <br />
<blockquote>He suggested two potential procedures.<br><br />
(i) Pick all the families with two children, one of which is a boy. If Mr. Smith is chosen randomly from this list, then the answer is 1/3.<br><br />
(ii) Pick a random family with two children; suppose the father is Mr. Smith. Then if the family has two boys, Mr. Smith says, “At least one of them is a boy.” If he has two girls, he says, “At least one of them is a girl.” If he has a boy and a girl he flips a coin to choose one or another of those two sentences. In this case the probability that both children are the same sex is 1/2.</blockquote><br />
Khovanova discusses a number of other scenarios related to being given both the sex and the day of the week on which the given child was born. The results may surprise students - and/or probability amateurs like this Chance contributor.<br />
<br />
The pdf file containing this article is accessible to all and contains active links to her references, which include two 2010 articles by Keith Devlin, both discussing day-of-the-week scenarios and real-life cultural differences which might impact solutions: [http://www.maa.org/devlin/devlin_04_10.html “Probability Can Bite”] and [http://www.maa.org/devlin/devlin_05_10.html “The Problem with Word Problems”]<br><br />
<br />
===Discussion===<br />
Do you think that Gardner made a mistake? Why or why not?<br><br />
<br />
Submitted by Margaret Cibes</div>Simon66217https://www.causeweb.org/wiki/chance/index.php?title=Chance_News_82&diff=15187Chance News 822012-02-15T22:23:50Z<p>Simon66217: /* Flood of data means flood of job opportunities */</p>
<hr />
<div>==Quotations==<br />
"I focus on the most important form of innumeracy in everyday life, statistical innumeracy--that is, the inability to reason about uncertainties and risk."<br />
<div align=right>--Gerd Gigerenzer</div><br />
<br />
Submitted by Bill Peterson<br />
<br />
==Forsooth==<br />
“[The] ballad ‘Someone Like you’ … has risen to near-iconic status recently, due in large part to its uncanny power to elicit tears and chills from listeners. …. Last year, [scientists] at McGill University reported that emotionally intense music releases dopamine in the pleasure and reward centers of the brain, similar to the effects of food, sex and drugs. …. Measuring listeners' responses, [the] team found that the <b><i>number of goose bumps observed</i></b> correlated with the amount of dopamine released, even when the music was extremely sad.”<br />
<div align=right>[http://online.wsj.com/article/SB10001424052970203646004577213010291701378.html?KEYWORDS=anatomy+of+tear-jerker “Anatomy of a Tear-Jerker”] (italics added)<br><br />
<i>The Wall Street Journal</i>, February 11, 2012</div><br />
Submitted by Margaret Cibes<br />
<br />
==Predictioneering==<br />
<br />
Bruce Bueno de Mesquita has written a fascinating, readable book, [http://www.predictioneersgame.com/ ''The Predictioneer’s Game: Using the Logic of Brazen Self-Interest to See and Shape the Future'']. A lengthy and generally positive review of Bueno de Mesquita’s views may be found in a NYT article, [http://www.nytimes.com/2009/08/16/magazine/16Bruce-t.html?_r=1&hpw=&pagewanted=all Can game theory predict when Iran will get the bomb?], by Clive Thompson (12 August 2009).<br />
<br />
His game-theory-based track record is indicated by:<br />
<blockquote><br />
For 29 years, Bueno de Mesquita has been developing and honing a computer model that predicts the outcome of any situation in which parties can be described as trying to persuade or coerce one another. Since the early 1980s, C.I.A. officials have hired him to perform more than a thousand predictions; a study by the C.I.A., now declassified, found that Bueno de Mesquita’s predictions “hit the bull’s-eye” twice as often as its own analysts did.<br />
</blockquote><br />
<br />
In the introduction to his book, Bueno de Mesquita says, “I have been predicting future events for three decades, often in print before the fact, and mostly getting them right.” Furthermore, “In my experience, government and private business want firm answers. They get plenty of wishy-washy predictions from their staff. They are looking for more than ‘On the one hand this, but on the other hand that’--and I give it to them.”<br />
<br />
===Discussion===<br />
<ol><br />
<li> In that NYT article may be found a statement shocking to the world of statistics and probability:<br />
<blockquote>Bueno de Mesquita does not express his forecasts in probabilistic terms; he says an event will transpire or it won’t. </blockquote><br />
Why is this a shocking statement to statisticians and probabilists?<br />
<br />
<li>In the NYT article is found the following criticism by Stephen Walt, a professor of international affairs at Harvard: <br />
<blockquote><br />
While Bueno de Mesquita has published many predictions in academic journals, the vast majority of his forecasts have been done in secret for corporate or government clients, where no independent academics can verify them. “We have no idea if he’s right 9 times out of 10, or 9 times out of a hundred, or 9 times out of a thousand,” Walt says. Walt also isn’t impressed by Stanley Feder’s C.I.A. study showing Bueno de Mesquita’s 90 percent hit rate. “It’s one midlevel C.I.A. bureaucrat saying, ‘This was a useful tool,’ ” Walt says.</blockquote><br />
Along these lines, suppose someone avers his hit rate is 100% when it involves forecasting a male birth, that is Prob (male predicted|male) = 1. Why might this be less than impressive?<br />
<br />
<li>Another critic may be found [http://decision-making.moshe-online.com/criticism_of_bueno_de_mesquita.html here] regarding a prediction about Libya. <blockquote><br />
In February 2011 Bueno de Mesquita predicted that the unrest in the Arab world will not spread to such places as Saudia Arabia and ... Libya. Yes, Libya. Watch and listen carefully to the segment starting at 1:51 min into the interview. <br />
</blockquote><br />
Other incorrect predictions made by Bueno de Mesquita are also noted on this web site, including what this author calls “The n factorial debacle” whereby Bueno de Mesquita misconstrues the number of possible interactions between n individuals (game participants). This web site also brings up the issue of the so-called “black swans” when it comes to predicting outcomes of the game. What is a black swan and why does a black swan have an impact on prediction?<br />
<br />
<li> Brazen Self-Interest and its mathematical logic rest on game theory which asserts that morality or any other nicety is counter productive to achieving success. Bueno de Mesquita’s particular computer model starts with data of expert opinion and then somehow via simulation iterates to a conclusion. Comment on the problem of local minimums/maximums.<br />
<br />
<li>Health care is in the news today as it was back in the 1990s. The NYT article notes that “In early 1993, a corporate client asked him to forecast whether the Clinton administration’s health care plan would pass, and he said it would.” The black swan in this instance was Congressman Daniel Rostenkowski who [page 125] “was the key to getting health care legislation through Congress.” Google Daniel Rostenkowski to see why Rostenkowski was a black swan and “contrary to my expectations, nothing passed through Congress.”<br />
</ol><br />
<br />
Submitted by Paul Alper<br />
<br />
==Flood of data means flood of job opportunities==<br />
<br />
[http://www.nytimes.com/2012/02/12/sunday-review/big-datas-impact-in-the-world.html The Age of Big Data], Steve Lohr, The New York Times, February 11, 2012.<br />
<br />
If you like working with data, you have great career opportunities ahead of you. We are seeing an<br />
<br />
<blockquote>an explosion of data, Web traffic and social network comments, as well as software and sensors that monitor shipments, suppliers and customer</blockquote><br />
<br />
This means a big deal for the job market.<br />
<br />
<blockquote>A report last year by the McKinsey Global Institute, the research arm of the consulting firm, projected that the United States needs 140,000 to 190,000 more workers with “deep analytical” expertise and 1.5 million more data-literate managers, whether retrained or hired.</blockquote><br />
<br />
It is a trend that occurs in more than business. This article cites major changes in Political Science and Public Health. The article introduces a term "big data" which it defines as<br />
<br />
<blockquote>shorthand for advancing trends in technology that open the door to a new approach to understanding the world and making decisions.</blockquote><br />
<br />
While the article extols the virtues of data analysis, for the most part, there are some cautionary statements.<br />
<br />
<blockquote>Big Data has its perils, to be sure. With huge data sets and fine-grained measurement, statisticians and computer scientists note, there is increased risk of “false discoveries.” The trouble with seeking a meaningful needle in massive haystacks of data, says Trevor Hastie, a statistics professor at Stanford, is that 'many bits of straw look like needles.' </blockquote><br />
<br />
Now data analysis demanding more attention from business circles and more.<br />
<br />
<blockquote>Veteran data analysts tell of friends who were long bored by discussions of their work but now are suddenly curious. “Moneyball” helped, they say, but things have gone way beyond that. “The culture has changed,” says Andrew Gelman, a statistician and political scientist at Columbia University. “There is this idea that numbers and statistics are interesting and fun. It’s cool now.”</blockquote><br />
<br />
==Martin Gardner's "mistake"==<br />
[http://docserver.ingentaconnect.com/deliver/connect/maa/07468342/v43n1/s6.pdf?expires=1329338290&id=67229128&titleid=75000908&accname=Guest+User&checksum=00F4070339B8BAA7FC302598A2063EA7 “Martin Gardner’s Mistake”]<br><br />
by Tanya Khovanova, <i>The College Mathematics Journal</i>, January 2012<br><br />
<br />
Martin Gardner first discussed the following problem in 1959:<br />
<blockquote>Mr. Smith has two children. At least one of them is a boy. What is the probability that both children are boys?</blockquote><br />
His answer at that time follows:<br />
<blockquote>If Smith has two children, at least one of which is a boy, we have three equally probable cases: boy-boy, boy-girl, girl-boy. In only one case are both children boys, so the probability that both are boys is 1/3.</blockquote><br />
Gardner later wrote a "correction" to his original solution, indicating that “the answer depends on the procedure by which the information is ‘at least one is a boy’ is obtained.” <br />
<blockquote>He suggested two potential procedures.<br><br />
(i) Pick all the families with two children, one of which is a boy. If Mr. Smith is chosen randomly from this list, then the answer is 1/3.<br><br />
(ii) Pick a random family with two children; suppose the father is Mr. Smith. Then if the family has two boys, Mr. Smith says, “At least one of them is a boy.” If he has two girls, he says, “At least one of them is a girl.” If he has a boy and a girl he flips a coin to choose one or another of those two sentences. In this case the probability that both children are the same sex is 1/2.</blockquote><br />
Khovanova discusses a number of other scenarios related to being given both the sex and the day of the week on which the given child was born. The results may surprise students - and/or probability amateurs like this Chance contributor.<br />
<br />
The pdf file containing this article is accessible to all and contains active links to her references, which include two 2010 articles by Keith Devlin, both discussing day-of-the-week scenarios and real-life cultural differences which might impact solutions: [http://www.maa.org/devlin/devlin_04_10.html “Probability Can Bite”] and [http://www.maa.org/devlin/devlin_05_10.html “The Problem with Word Problems”]<br><br />
<br />
===Discussion===<br />
Do you think that Gardner made a mistake? Why or why not?<br><br />
<br />
Submitted by Margaret Cibes</div>Simon66217https://www.causeweb.org/wiki/chance/index.php?title=Chance_News_82&diff=15155Chance News 822012-02-14T18:33:02Z<p>Simon66217: /* Flood of data means flood of job opportunities */</p>
<hr />
<div>==Quotations==<br />
==Forsooth==<br />
<br />
==Predictioneering==<br />
<br />
Bruce Bueno de Mesquita has written a fascinating, readable book, [http://www.predictioneersgame.com/ ''The Predictioneer’s Game: Using the Logic of Brazen Self-Interest to See and Shape the Future'']. A lengthy and generally positive review of Bueno de Mesquita’s views may be found in a NYT article, [http://www.nytimes.com/2009/08/16/magazine/16Bruce-t.html?_r=1&hpw=&pagewanted=all Can game theory predict when Iran will get the bomb?], by Clive Thompson (12 August 2009).<br />
<br />
His game-theory-based track record is indicated by <br />
<blockquote><br />
For 29 years, Bueno de Mesquita has been developing and honing a computer model that predicts the outcome of any situation in which parties can be described as trying to persuade or coerce one another. Since the early 1980s, C.I.A. officials have hired him to perform more than a thousand predictions; a study by the C.I.A., now declassified, found that Bueno de Mesquita’s predictions “hit the bull’s-eye” twice as often as its own analysts did.<br />
</blockquote><br />
<br />
In the introduction to his book, Bueno de Mesquita says, “I have been predicting future events for three decades, often in print before the fact, and mostly getting them right.” Furthermore, “In my experience, government and private business want firm answers. They get plenty of wishy-washy predictions from their staff. They are looking for more than ‘On the one hand this, but on the other hand that’--and I give it to them.”<br />
<br />
===Discussion===<br />
<ol><br />
<li> In that NYT article may be found a statement shocking to the world of statistics and probability:<br />
<blockquote>Bueno de Mesquita does not express his forecasts in probabilistic terms; he says an event will transpire or it won’t. </blockquote><br />
Why is this a shocking statement to statisticians and probabilists?<br />
<br />
<li>In the NYT article is found the following criticism by Stephen Walt, a professor of international affairs at Harvard: <br />
<blockquote><br />
While Bueno de Mesquita has published many predictions in academic journals, the vast majority of his forecasts have been done in secret for corporate or government clients, where no independent academics can verify them. “We have no idea if he’s right 9 times out of 10, or 9 times out of a hundred, or 9 times out of a thousand,” Walt says. Walt also isn’t impressed by Stanley Feder’s C.I.A. study showing Bueno de Mesquita’s 90 percent hit rate. “It’s one midlevel C.I.A. bureaucrat saying, ‘This was a useful tool,’ ” Walt says.</blockquote><br />
Along these lines, suppose someone avers his hit rate is 100% when it involves forecasting a male birth, that is Prob (male predicted|male) = 1. Why might this be less than impressive?<br />
<br />
<li>Another critic may be found [http://decision-making.moshe-online.com/criticism_of_bueno_de_mesquita.html here] regarding a prediction about Libya. <blockquote><br />
In February 2011 Bueno de Mesquita predicted that the unrest in the Arab world will not spread to such places as Saudia Arabia and ... Libya. Yes, Libya. Watch and listen carefully to the segment starting at 1:51 min into the interview. <br />
</blockquote><br />
Other incorrect predictions made by Bueno de Mesquita are also noted on this web site, including what this author calls “The n factorial debacle” whereby Bueno de Mesquita misconstrues the number of possible interactions between n individuals (game participants). This web site also brings up the issue of the so-called “black swans” when it comes to predicting outcomes of the game. What is a black swan and why does a black swan have an impact on prediction?<br />
<br />
<li> Brazen Self-Interest and its mathematical logic rest on game theory which asserts that morality or any other nicety is counter productive to achieving success. Bueno de Mesquita’s particular computer model starts with data of expert opinion and then somehow via simulation iterates to a conclusion. Comment on the problem of local minimums/maximums.<br />
<br />
<li>Health care is in the news today as it was back in the 1990s. The NYT article notes that “In early 1993, a corporate client asked him to forecast whether the Clinton administration’s health care plan would pass, and he said it would.” The black swan in this instance was Congressman Daniel Rostenkowski who [page 125] “was the key to getting health care legislation through Congress.” Google Daniel Rostenkowski to see why Rostenkowski was a black swan and “contrary to my expectations, nothing passed through Congress.”<br />
</ol><br />
<br />
Submitted by Paul Alper<br />
<br />
==Flood of data means flood of job opportunities==<br />
<br />
[http://www.nytimes.com/2012/02/12/sunday-review/big-datas-impact-in-the-world.html The Age of Big Data], Steve Lohr, The New York Times, February 11, 2012.<br />
<br />
We are seeing an<br />
<br />
<blockquote>an explosion of data, Web traffic and social network comments, as well as software and sensors that monitor shipments, suppliers and customer</blockquote><br />
<br />
This means a big deal for the job market.<br />
<br />
<blockquote>A report last year by the McKinsey Global Institute, the research arm of the consulting firm, projected that the United States needs 140,000 to 190,000 more workers with “deep analytical” expertise and 1.5 million more data-literate managers, whether retrained or hired.</blockquote><br />
<br />
It is a trend that occurs in more than business. This article cites major changes in Political Science and Public Health. The article introduces a term "big data" which it defines as<br />
<br />
<blockquote>shorthand for advancing trends in technology that open the door to a new approach to understanding the world and making decisions.</blockquote></div>Simon66217https://www.causeweb.org/wiki/chance/index.php?title=Chance_News_82&diff=15154Chance News 822012-02-14T18:19:19Z<p>Simon66217: /* Item 2 */</p>
<hr />
<div>==Quotations==<br />
==Forsooth==<br />
<br />
==Predictioneering==<br />
<br />
Bruce Bueno de Mesquita has written a fascinating, readable book, [http://www.predictioneersgame.com/ ''The Predictioneer’s Game: Using the Logic of Brazen Self-Interest to See and Shape the Future'']. A lengthy and generally positive review of Bueno de Mesquita’s views may be found in a NYT article, [http://www.nytimes.com/2009/08/16/magazine/16Bruce-t.html?_r=1&hpw=&pagewanted=all Can game theory predict when Iran will get the bomb?], by Clive Thompson (12 August 2009).<br />
<br />
His game-theory-based track record is indicated by <br />
<blockquote><br />
For 29 years, Bueno de Mesquita has been developing and honing a computer model that predicts the outcome of any situation in which parties can be described as trying to persuade or coerce one another. Since the early 1980s, C.I.A. officials have hired him to perform more than a thousand predictions; a study by the C.I.A., now declassified, found that Bueno de Mesquita’s predictions “hit the bull’s-eye” twice as often as its own analysts did.<br />
</blockquote><br />
<br />
In the introduction to his book, Bueno de Mesquita says, “I have been predicting future events for three decades, often in print before the fact, and mostly getting them right.” Furthermore, “In my experience, government and private business want firm answers. They get plenty of wishy-washy predictions from their staff. They are looking for more than ‘On the one hand this, but on the other hand that’--and I give it to them.”<br />
<br />
===Discussion===<br />
<ol><br />
<li> In that NYT article may be found a statement shocking to the world of statistics and probability:<br />
<blockquote>Bueno de Mesquita does not express his forecasts in probabilistic terms; he says an event will transpire or it won’t. </blockquote><br />
Why is this a shocking statement to statisticians and probabilists?<br />
<br />
<li>In the NYT article is found the following criticism by Stephen Walt, a professor of international affairs at Harvard: <br />
<blockquote><br />
While Bueno de Mesquita has published many predictions in academic journals, the vast majority of his forecasts have been done in secret for corporate or government clients, where no independent academics can verify them. “We have no idea if he’s right 9 times out of 10, or 9 times out of a hundred, or 9 times out of a thousand,” Walt says. Walt also isn’t impressed by Stanley Feder’s C.I.A. study showing Bueno de Mesquita’s 90 percent hit rate. “It’s one midlevel C.I.A. bureaucrat saying, ‘This was a useful tool,’ ” Walt says.</blockquote><br />
Along these lines, suppose someone avers his hit rate is 100% when it involves forecasting a male birth, that is Prob (male predicted|male) = 1. Why might this be less than impressive?<br />
<br />
<li>Another critic may be found [http://decision-making.moshe-online.com/criticism_of_bueno_de_mesquita.html here] regarding a prediction about Libya. <blockquote><br />
In February 2011 Bueno de Mesquita predicted that the unrest in the Arab world will not spread to such places as Saudia Arabia and ... Libya. Yes, Libya. Watch and listen carefully to the segment starting at 1:51 min into the interview. <br />
</blockquote><br />
Other incorrect predictions made by Bueno de Mesquita are also noted on this web site, including what this author calls “The n factorial debacle” whereby Bueno de Mesquita misconstrues the number of possible interactions between n individuals (game participants). This web site also brings up the issue of the so-called “black swans” when it comes to predicting outcomes of the game. What is a black swan and why does a black swan have an impact on prediction?<br />
<br />
<li> Brazen Self-Interest and its mathematical logic rest on game theory which asserts that morality or any other nicety is counter productive to achieving success. Bueno de Mesquita’s particular computer model starts with data of expert opinion and then somehow via simulation iterates to a conclusion. Comment on the problem of local minimums/maximums.<br />
<br />
<li>Health care is in the news today as it was back in the 1990s. The NYT article notes that “In early 1993, a corporate client asked him to forecast whether the Clinton administration’s health care plan would pass, and he said it would.” The black swan in this instance was Congressman Daniel Rostenkowski who [page 125] “was the key to getting health care legislation through Congress.” Google Daniel Rostenkowski to see why Rostenkowski was a black swan and “contrary to my expectations, nothing passed through Congress.”<br />
</ol><br />
<br />
Submitted by Paul Alper<br />
<br />
==Flood of data means flood of job opportunities==<br />
<br />
[http://www.nytimes.com/2012/02/12/sunday-review/big-datas-impact-in-the-world.html The Age of Big Data], Steve Lohr, The New York Times, February 11, 2012.</div>Simon66217