Difference between revisions of "Sandbox"

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==Friendship==
 
  
Except for Voltaire who famously (albeit, possibly apocryphally) said, “Lord, protect me from my friends; I can take care of my enemies,” few doubt the benefits of having friends. 
 
  
[http://www.nytimes.com/2009/04/21/health/21well.html?_r=1&8dpc From Tara Parker-Pope] we find some surprising side effects of friendship.  She suggests looking at an Australian study which “found that older people with a large circle of friends were 22 percent less likely to die during the study period than those with fewer friends.”  Further, “last year, [http://www.ajph.org/cgi/content/abstract/AJPH.2007.113654v1 Harvard researchers] reported that strong social ties could promote brain health as we age.” 
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==Forsooth==
  
She also refers to a 2006 study [http://jco.ascopubs.org/cgi/content/full/24/7/1105 of nearly 3000 nurses] with breast cancer which “found that women without close friends were four times as likely to die from the disease as women with 10 or more friends. And notably, proximity and the amount of contact with a friend wasn’t associated with survival. Just having friends was protective.” She closes her article with a quote from the director of the center for gerontology at Virginia Tech: “People with stronger friendship networks feel like there is someone they can turn to. Friendship is an undervalued resource. The consistent message of these studies is that friends make your life better.”
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==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.”
 +
<div align=right>-- Donald Kennedy (former president of Stanford University), ''Academic Duty'', Harvard University Press, 1997, p.17</div>
  
Discussion
+
----
  
1.  Parker-Pope also mentioned researchers [http://www.psy.plymouth.ac.uk/research/ece/publications/pdf/Social-Support-and-Slant.pdf here] who “studied 34 students at the University of Virginia, taking them to the base of a steep hill and fitting them with a weighted backpack. They were then asked to estimate the steepness of the hill. Some participants stood next to friends during the exercise, while others were alone.  The students who stood with friends gave lower estimates of the steepness of the hill. And the longer the friends had known each other, the less steep the hill appeared.”  In fact, three of the 34 were excluded because they were deemed outliers. The participants estimated the slant via three different methods as can be seen in the figure below:
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"Using scientific language and measurement doesn’t prevent a researcher from conducting flawed experiments and drawing wrong conclusions — especially when they confirm preconceptions."
  
http://www.dartmouth.edu/~chance/forwiki/friendship1.jpg
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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>
  
The “haptic” measurement “"required adjusting a tilt board with a palm rest to be parallel to the hill, importantly, without looking at one’s hand."”  As can seen from the above figure, it appears to more accurate than either the “verbal,” merely a guess, or the “visual” which a (presumably crude) disk-like device acted as an aide.
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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>
The researchers performed a two-way ANOVA (sex and social support) separately for each of the three measuring methods.  They reported the value of each F(1,27) to determine a p-value for each method to see if Friend compared to Alone is statistically significant.  So, why the number “27”?  From merely looking at the figure, which of the three methods for determining slant would appear to be unrelated to friendship?
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by Gary Greenberg, ''New York Times Magazine'', 7 November 2018
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2.  The above study took place in Virginia.  In Plymouth, England the researchers did a similar slant study but this time instead of friendship directly, imagining of support, was tested as can be seen from the following figure:
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[https://www.nytimes.com/2019/07/17/opinion/pretrial-ai.html The Problems With Risk Assessment Tools]<br>
 +
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]<br>
 +
: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
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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>
 +
:by Glenn Kessler, ''Washington Post'', 1 June 2018
 +
 +
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.
 +
 +
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.
 +
[https://prstudy.publichealth.gwu.edu/ George Washington University study]
 +
 +
:[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>
 +
: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>
 +
[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>
 +
[https://www.nhc.noaa.gov/aboutcone.shtml Definition of the NHC Track Forecast Cone]
 +
----
 +
[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.]
 +
''Popular Science'', 10 September 2018
 +
----
 +
[https://www.economist.com/united-states/2018/08/30/googling-the-news Googling the news]<br>
 +
''Economist'', 1 September 2018
 +
 +
[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]
 +
----
 +
[http://www.wyso.org/post/stats-stories-reading-writing-and-risk-literacy Reading , Writing and Risk Literacy]
 +
 +
[http://www.riskliteracy.org/]
 +
-----
 +
[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.]
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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>
 +
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<math>P(E)  = {n \choose k} p^k (1-p)^{ n-k}</math>
 +
 +
<math>\hat{p}(H|H)</math>
 +
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<math>\hat{p}(H|HH)</math>
 +
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==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.
 +
<center>[[File:BrokenTile.jpg | 400px]]</center>
 +
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.
  
 +
<center>
 +
{| class="wikitable"
 +
|-
 +
! Piece !! Sq. Inches !! % of Total
 +
|-
 +
| 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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| 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%
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|-
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| 8 || 3.03 || 2.2%
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|-
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| 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.”
  
This study had 36 participants and similarly to the first study, they did a two-way ANOVA (sex and imagery of support) leading to F(2,30) for each slant measuring technique. So, why the “2” and the “30”?  From merely looking at the figure, which of the three methods for determining slant would appear to be unrelated to imagery of support?
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<center>[[File:Montante_plot2.png | 500px]]</center>
 
   
 
   
3. In either study, “visual” or “verbal” on average markedly overstate the slant of the hill. What does that suggest about people’s ability to judge a task?
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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.
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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.
4The researchers admit that for either study, “"Participants in this study were not randomly assigned."”  Why would this pose a problem?
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Submitted by William Montante
5. To give Voltaire his due, Parker-Pope points out that  [http://content.nejm.org/cgi/content/full/357/4/370 “A large 2007 study] showed an increase of nearly 60 percent in the risk for obesity among people whose friends gained weight.”
 
  
Submitted by Paul Alper
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----

Latest revision as of 20:58, 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