Difference between revisions of "Sandbox"

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==Vampirical==
 
  
The following quotation can be found [http://www.stat.columbia.edu/~gelman/research/unpublished/power.pdf here] in an article by Gelman and Weakliem entitled, "Of beauty, sex and power: Statistical challenges in estimating small effects":
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==Forsooth==
 +
 
 +
==Quotations==
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“We know that people tend to overestimate the frequency of well-publicized, spectacular
 +
events compared with more commonplace ones; this is a well-understood phenomenon in
 +
the literature of risk assessment and leads to the truism that when statistics plays folklore,
 +
folklore always wins in a rout.”
 +
<div align=right>-- Donald Kennedy (former president of Stanford University), ''Academic Duty'', Harvard University Press, 1997, p.17</div>
 +
 
 +
----
 +
 
 +
"Using scientific language and measurement doesn’t prevent a researcher from conducting flawed experiments and drawing wrong conclusions — especially when they confirm preconceptions."
 +
 
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<div align=right>-- Blaise Agüera y Arcas, Margaret Mitchell and Alexander Todoorov, quoted in: The racist history behind facial recognition, ''New York Times'', 10 July 2019</div>
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==In progress==
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[https://www.nytimes.com/2018/11/07/magazine/placebo-effect-medicine.html What if the Placebo Effect Isn’t a Trick?]<br>
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by Gary Greenberg, ''New York Times Magazine'', 7 November 2018
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 +
[https://www.nytimes.com/2019/07/17/opinion/pretrial-ai.html The Problems With Risk Assessment Tools]<br>
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by Chelsea Barabas, Karthik Dinakar and Colin Doyle, ''New York Times'', 17 July 2019
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==Hurricane Maria deaths==
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Laura Kapitula sent the following to the Isolated Statisticians e-mail list:
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 +
:[Why counting casualties after a hurricane is so hard]<br>
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:by Jo Craven McGinty, Wall Street Journal, 7 September 2018
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The article is subtitled: Indirect deaths—such as those caused by gaps in medication—can occur months after a storm, complicating tallies
 
   
 
   
This ability of the theory to explain findings in any direction is also pointed out by Freese (2007), who describes this sort of argument as "more 'vampirical' than 'empirical'--unable to be killed by mere evidence."
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Laura noted that
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:[https://www.washingtonpost.com/news/fact-checker/wp/2018/06/02/did-4645-people-die-in-hurricane-maria-nope/?utm_term=.0a5e6e48bf11 Did 4,645 people die in Hurricane Maria? Nope.]<br>
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:by Glenn Kessler, ''Washington Post'', 1 June 2018
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The source of the 4645 figure is a [https://www.nejm.org/doi/full/10.1056/NEJMsa1803972 NEJM article].  Point estimate, the 95% confidence interval ran from 793 to 8498.
 +
 
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President Trump has asserted that the actual number is
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[https://twitter.com/realDonaldTrump/status/1040217897703026689 6 to 18].
 +
The ''Post'' article notes that Puerto Rican official had asked researchers at George Washington University to do an estimate of the death toll.  That work is not complete.
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[https://prstudy.publichealth.gwu.edu/ George Washington University study]
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:[https://fivethirtyeight.com/features/we-still-dont-know-how-many-people-died-because-of-katrina/?ex_cid=538twitter We sttill don’t know how many people died because of Katrina]<br>
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:by Carl Bialik, FiveThirtyEight, 26 August 2015
 +
 
 +
----
 +
[https://www.nytimes.com/2018/09/11/climate/hurricane-evacuation-path-forecasts.html These 3 Hurricane Misconceptions Can Be Dangerous. Scientists Want to Clear Them Up.]<br>
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[https://journals.ametsoc.org/doi/abs/10.1175/BAMS-88-5-651 Misinterpretations of the “Cone of Uncertainty” in Florida during the 2004 Hurricane Season]<br>
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[https://www.nhc.noaa.gov/aboutcone.shtml Definition of the NHC Track Forecast Cone]
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----
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[https://www.popsci.com/moderate-drinking-benefits-risks Remember when a glass of wine a day was good for you? Here's why that changed.]
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''Popular Science'', 10 September 2018
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----
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[https://www.economist.com/united-states/2018/08/30/googling-the-news Googling the news]<br>
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''Economist'', 1 September 2018
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[https://www.cnbc.com/2018/09/17/google-tests-changes-to-its-search-algorithm-how-search-works.html We sat in on an internal Google meeting where they talked about changing the search algorithm — here's what we learned]
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----
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[http://www.wyso.org/post/stats-stories-reading-writing-and-risk-literacy Reading , Writing and Risk Literacy]
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[http://www.riskliteracy.org/]
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-----
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[https://twitter.com/i/moments/1025000711539572737?cn=ZmxleGlibGVfcmVjc18y&refsrc=email Today is the deadliest day of the year for car wrecks in the U.S.]
  
Gelman and Weakliem are criticizing research which putatively detects an effect merely because statistical significance is obtained on either side of zero or, in the case of ratio of females to males, 50%.  In particular, they contest the results of studies which claim that “beautiful parents have more daughters, violent men have more sons and other sex-related patterns.”  They also analyze so-called Type M (magnitude) errors and Type S (sign) errors.
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==Some math doodles==
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<math>P \left({A_1 \cup A_2}\right) = P\left({A_1}\right) + P\left({A_2}\right) -P \left({A_1 \cap A_2}\right)</math>
  
This is a Type M (magnitude) error: the study is constructed in such a way that any statistically-significant finding will almost certainly be a huge overestimate of the true effect.  In addition there will be Type S (sign) errors, in which the estimate will be in the opposite direction as the true effect.
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<math>P(E)   = {n \choose k} p^k (1-p)^{ n-k}</math>
  
Discussion
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<math>\hat{p}(H|H)</math>
  
1. As a long-term research project, determine via literature and art how the notion of “beautiful” has changed through the ages and across cultures.
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<math>\hat{p}(H|HH)</math>
  
2.  The imbalance between baby daughters and baby sons produced by beautiful people somehow went from the original article’s (not statistically significant) 4.7% to 8% when dealing with the largest comparison (the most beautiful parents on a scale of 1 to 5) to 26% and finally to 36% via a typo in the New York Times. 
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==Accidental insights==
  
3.  The authors, based on their analysis, say “There is no compelling evidence that “Beautiful parents produce more daughters.”  Nevertheless, why did the original paper have so much appeal?
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My collective understanding of Power Laws would fit beneath the shallow end of the long tail. Curiosity, however, easily fills the fat end.  I long have been intrigued by the concept and the surprisingly common appearance of power laws in varied natural, social and organizational dynamics.  But, am I just seeing a statistical novelty or is there meaning and utility in Power Law relationships? Here’s a case in point.
 +
 
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While carrying a pair of 10 lb. hand weights one, by chance, slipped from my grasp and fell onto a piece of ceramic tile I had left on the carpeted floor. The fractured tile was inconsequential, meant for the trash.
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<center>[[File:BrokenTile.jpg | 400px]]</center>
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As I stared, slightly annoyed, at the mess, a favorite maxim of the Greek philosopher, Epictetus, came to mind: “On the occasion of every accident that befalls you, turn to yourself and ask what power you have to put it to use.”  Could this array of large and small polygons form a Power Law? With curiosity piqued, I collected all the fragments and measured the area of each piece.
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 +
<center>
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{| class="wikitable"
 +
|-
 +
! Piece !! Sq. Inches !! % of Total
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|-
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| 1 || 43.25 || 31.9%
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|-
 +
| 2 || 35.25 ||26.0%
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|-
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|  3 || 23.25 || 17.2%
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|-
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| 4 || 14.10 || 10.4%
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|-
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| 5 || 7.10 || 5.2%
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|-
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| 6 || 4.70 || 3.5%
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|-
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| 7 || 3.60 || 2.7%
 +
|-
 +
| 8 || 3.03 || 2.2%
 +
|-
 +
| 9 || 0.66 || 0.5%
 +
|-
 +
| 10 || 0.61 || 0.5%
 +
|}
 +
</center>
 +
<center>[[File:Montante_plot1.png | 500px]]</center>
 +
The data and plot look like a Power Law distribution. The first plot is an exponential fit of percent total area. The second plot is same data on a log normal format. Clue: Ok, data fits a straight line.  I found myself again in the shallow end of the knowledge curve. Does the data reflect a Power Law or something else, and if it does what does it reflect?  What insights can I gain from this accident? Favorite maxims of Epictetus and Pasteur echoed in my head:
 +
“On the occasion of every accident that befalls you, remember to turn to yourself and inquire what power you have to turn it to use” and “Chance favors only the prepared mind.”
 +
 
 +
<center>[[File:Montante_plot2.png | 500px]]</center>
 +
   
 +
My “prepared” mind searched for answers, leading me down varied learning paths. Tapping the power of networks, I dropped a note to Chance News editor Bill Peterson. His quick web search surfaced a story from ''Nature News'' on research by Hans Herrmann, et. al. [http://www.nature.com/news/2004/040227/full/news040223-11.html Shattered eggs reveal secrets of explosions].  As described there, researchers have found power-law relationships for the fragments produced by shattering a pane of glass or breaking a solid object, such as a stone. Seems there is a science underpinning how things break and explode; potentially useful in Forensic reconstructions.
 +
Bill also provided a link to [http://cran.r-project.org/web/packages/poweRlaw/vignettes/poweRlaw.pdf a vignette from CRAN] describing a maximum likelihood procedure for fitting a Power Law relationship. I am now learning my way through that.
  
4. As a check, the authors used People magazine’s “list of the fifty most beautiful people” from 1995 to 2000 to find the offsprings.  There were “157 girls out of 329 children, or 47.7% girls (with a standard error 2.8%).”  Instead of more females, fewer were produced.
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Submitted by William Montante
  
5. The authors note “the structure of scientific publication and media attention seem to have a biasing effect on social science research.”  Explain what they mean by a “biasing effect.”
+
----

Latest revision as of 15:58, 17 July 2019


Forsooth

Quotations

“We know that people tend to overestimate the frequency of well-publicized, spectacular events compared with more commonplace ones; this is a well-understood phenomenon in the literature of risk assessment and leads to the truism that when statistics plays folklore, folklore always wins in a rout.”

-- Donald Kennedy (former president of Stanford University), Academic Duty, Harvard University Press, 1997, p.17

"Using scientific language and measurement doesn’t prevent a researcher from conducting flawed experiments and drawing wrong conclusions — especially when they confirm preconceptions."

-- Blaise Agüera y Arcas, Margaret Mitchell and Alexander Todoorov, quoted in: The racist history behind facial recognition, New York Times, 10 July 2019

In progress

What if the Placebo Effect Isn’t a Trick?
by Gary Greenberg, New York Times Magazine, 7 November 2018

The Problems With Risk Assessment Tools
by Chelsea Barabas, Karthik Dinakar and Colin Doyle, New York Times, 17 July 2019

Hurricane Maria deaths

Laura Kapitula sent the following to the Isolated Statisticians e-mail list:

[Why counting casualties after a hurricane is so hard]
by Jo Craven McGinty, Wall Street Journal, 7 September 2018

The article is subtitled: Indirect deaths—such as those caused by gaps in medication—can occur months after a storm, complicating tallies

Laura noted that

Did 4,645 people die in Hurricane Maria? Nope.
by Glenn Kessler, Washington Post, 1 June 2018

The source of the 4645 figure is a NEJM article. Point estimate, the 95% confidence interval ran from 793 to 8498.

President Trump has asserted that the actual number is 6 to 18. The Post article notes that Puerto Rican official had asked researchers at George Washington University to do an estimate of the death toll. That work is not complete. George Washington University study

We sttill don’t know how many people died because of Katrina
by Carl Bialik, FiveThirtyEight, 26 August 2015

These 3 Hurricane Misconceptions Can Be Dangerous. Scientists Want to Clear Them Up.
Misinterpretations of the “Cone of Uncertainty” in Florida during the 2004 Hurricane Season
Definition of the NHC Track Forecast Cone


Remember when a glass of wine a day was good for you? Here's why that changed. Popular Science, 10 September 2018


Googling the news
Economist, 1 September 2018

We sat in on an internal Google meeting where they talked about changing the search algorithm — here's what we learned


Reading , Writing and Risk Literacy

[1]


Today is the deadliest day of the year for car wrecks in the U.S.

Some math doodles

<math>P \left({A_1 \cup A_2}\right) = P\left({A_1}\right) + P\left({A_2}\right) -P \left({A_1 \cap A_2}\right)</math>

<math>P(E) = {n \choose k} p^k (1-p)^{ n-k}</math>

<math>\hat{p}(H|H)</math>

<math>\hat{p}(H|HH)</math>

Accidental insights

My collective understanding of Power Laws would fit beneath the shallow end of the long tail. Curiosity, however, easily fills the fat end. I long have been intrigued by the concept and the surprisingly common appearance of power laws in varied natural, social and organizational dynamics. But, am I just seeing a statistical novelty or is there meaning and utility in Power Law relationships? Here’s a case in point.

While carrying a pair of 10 lb. hand weights one, by chance, slipped from my grasp and fell onto a piece of ceramic tile I had left on the carpeted floor. The fractured tile was inconsequential, meant for the trash.

BrokenTile.jpg

As I stared, slightly annoyed, at the mess, a favorite maxim of the Greek philosopher, Epictetus, came to mind: “On the occasion of every accident that befalls you, turn to yourself and ask what power you have to put it to use.” Could this array of large and small polygons form a Power Law? With curiosity piqued, I collected all the fragments and measured the area of each piece.

Piece Sq. Inches % of Total
1 43.25 31.9%
2 35.25 26.0%
3 23.25 17.2%
4 14.10 10.4%
5 7.10 5.2%
6 4.70 3.5%
7 3.60 2.7%
8 3.03 2.2%
9 0.66 0.5%
10 0.61 0.5%
Montante plot1.png

The data and plot look like a Power Law distribution. The first plot is an exponential fit of percent total area. The second plot is same data on a log normal format. Clue: Ok, data fits a straight line. I found myself again in the shallow end of the knowledge curve. Does the data reflect a Power Law or something else, and if it does what does it reflect? What insights can I gain from this accident? Favorite maxims of Epictetus and Pasteur echoed in my head: “On the occasion of every accident that befalls you, remember to turn to yourself and inquire what power you have to turn it to use” and “Chance favors only the prepared mind.”

Montante plot2.png

My “prepared” mind searched for answers, leading me down varied learning paths. Tapping the power of networks, I dropped a note to Chance News editor Bill Peterson. His quick web search surfaced a story from Nature News on research by Hans Herrmann, et. al. Shattered eggs reveal secrets of explosions. As described there, researchers have found power-law relationships for the fragments produced by shattering a pane of glass or breaking a solid object, such as a stone. Seems there is a science underpinning how things break and explode; potentially useful in Forensic reconstructions. Bill also provided a link to a vignette from CRAN describing a maximum likelihood procedure for fitting a Power Law relationship. I am now learning my way through that.

Submitted by William Montante