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Forsooth

COLONEL [Buzz] ALDRIN: Infinity and beyond. (Laughter.)

THE PRESIDENT: This is infinity here. It could be infinity. We don’t really don’t know. But it could be. It has to be something -- but it could be infinity, right?

Okay. (Applause.)

in: Remarks by the President signing an Executive Order on the National Space Council
Office of the White House Press Secretary, 30 June 2017.

Suggested by Mike Olinick

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

Rats, again!

De Blasio wants to dramatically reduce NYC’s rat population. Don’t hold your breath.
by Jonathan Auerbach, Slate, 21 July 2017

New York City mayor Bill DiBlasio has announced a $32 million plan to reduce the city's rat population. In Chance News 102, we described a capture-recapture experiment conducted by Auerbach to estimate the size of that population, which disputed the folk wisdom that the city had more rates than people. But in any case, reducing the number sounds like a good idea.

In his Slate article, Auerbach questions the mayor's enthusiastic goals of a 70% reduction in select areas, which he likens to other impressive-sounding claims about improvements in homicides, violent crime and pedestrian fatalities. He worries that by responding to extreme events, the apparent improvements may be due to the regression effect. The article alluded to Galton's famous data on heritability of height, where he found that children of taller parents tended to be above average in height, though not by quite as much as their parents. (The BMJ has a good tutorial on regression to the mean that gives more detail on Galton's findings; a followup piece gives further examples of the phenomenon.)

The Slate article provides a number of data graphics to illustrate the problem. For example. reproduced below is analysis of the mayor's Action Plan for Neighborhood Safety, implemented in 2014. The leftmost panel shows the 5-year trend in the violent crime in 13 neighborhoods targeted by the program from 2011 through 2015. The drop in 2015 was cited as evidence of the success of the program.

NYC crime Slate.png

But numbers of violent crimes fluctuate from year to year. The next two panels show what happened in the 13 highest crime developments from 2013 and 2012, respectively. Of course, the mayor's program was not in force in those year. Yet In both cases, the high-crime developments saw a drop in crime the following year that was comparable in magnitude that was observed in the neighborhoods targeted by the program. This demonstrates that the progress attributed to the program could simply be a statistical artifact.

The article notes that the Nobel Prize winning economist Milton Friedman described the regression fallacy by saying "I suspect that the regression is the most common fallacy in the statistical analysis of economic data."

Discussion
The caption to the graphs shown above reads, "All three panels show increases from 2011 to the year in which the developments were selected, before finally reverting toward their 2011 levels by 2015 (emphasis added). What do you make of this last comment?

Submitted by Bill Peterson

In progress

Lies, Damned Lies And Statistics: How Bad Statistics Are Feeding Fake News
by Kalev Leetaru, Forbes, "Big Data" blog, 2 February 2017


Statistical errors are often not due to mathematical errors
by Brian Zaharatos, letter to the editor, Chronicle of Higher Education, 11 July 2017

Zaharatos writes in response to a June 28 article from the Chronicle, entitled A new theory on how researchers can solve the reproducibility crisis: Do the math. While acknowledging some good points raised in the article, he notes that the arguments were undercut by some misinterpretations of statistical concepts, such as this faulty description of significance:

[F]or a p-value of 0.05…a study’s finding will be deemed significant if researchers identify a 95-percent chance that it is genuine.

Regarding the headline, he notes that people's difficulties interpreting statistical studies are not purely mathematical: "Mathematical skills are necessary for success in statistics, but they are far from sufficient. Statisticians — and researchers using statistics — ought to have a nuanced understanding of the concepts that mathematics helps them quantify."



Jeff Sessions used our research to claim that sanctuary cities have more crime. He’s wrong.
by Loren Collingwood and Benjamin Gonzalez-O'Brien, Washington Post, "Monkey Cage" blog, 14 July 2017

Sally Hernandez says cities labeled as sanctuaries have less crime, according to FBI statistics
by W. Gardner Selby, Politifact, 10 May 2017

Half-True Hernandez "FBI crime statistics have found that labeled ‘sanctuary’ cities experience lower rates of all crime types, including homicides."

— Sally Hernandez on Tuesday, April 18th, 2017 in an oped commentary co-authored by four other Texas sheriffs Sally Hernandez says cities labeled as sanctuaries have less crime, according to FBI statistics



Debate is over: Gerrymandering is crucial to G.O.P.’s hold on House
by Nate Cohn, New York Times, "TheUpshot" blog, 2 August 2017

Chance of gun death

http://www.nytimes.com/2015/12/05/upshot/in-other-countries-youre-as-likely-to-be-killed-by-a-falling-object-as-a-gun.html?rref=upshot&module=Ribbon&version=context&region=Header&action=click&contentCollection=The%20Upshot&pgtype=Multimedia


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