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by David Spiegelhalter, ''The Guardian'', 17 July 2016 | by David Spiegelhalter, ''The Guardian'', 17 July 2016 | ||
Published in the wake of the Brexit debate, but obviously applicable to upcoming US presidential election, the article offers these nine strategies for twisting numbers to back a specious claim. | |||
* Use a real number, but change its meaning | * Use a real number, but change its meaning | ||
| Line 41: | Line 42: | ||
* Prematurely announce the success of a policy initiative using unofficial selected data | * Prematurely announce the success of a policy initiative using unofficial selected data | ||
* If all else fails, just make the numbers up | * If all else fails, just make the numbers up | ||
To be continued... | |||
Submitted by Bill Peterson | |||
==Statistical reasoning in journalism education== | ==Statistical reasoning in journalism education== | ||
Revision as of 18:54, 24 August 2016
False alarm on prostate cancer
Flawed study of advanced prostate cancer spreads false alarm
Cancer prevention
Helpless to prevent cancer? Actually, quite a bit Is in your control
By Aooron E. Carroll, TheUpshot blog, New York Times, 5 July 2016
Explaining
We Have Some Serious Explaining to Do
by Rob Santos, AMSTAT News, 1 July 2106
Conflicting polls
Jeff Witmer sent the following to the Isolated Statisticians list:
- Confused by contradictory polls? Take a step back
- by Nate Cohn, "Upshot" blog, New York Times, 18 July 2016
In progress
http://fivethirtyeight.com/features/the-polls-arent-skewed-trump-really-is-losing-badly/
Guide to bad statistics
Our nine-point guide to spotting a dodgy statistic
by David Spiegelhalter, The Guardian, 17 July 2016
Published in the wake of the Brexit debate, but obviously applicable to upcoming US presidential election, the article offers these nine strategies for twisting numbers to back a specious claim.
- Use a real number, but change its meaning
- Make the number look big (but not too big)
- Casually imply causation from correlation
- Choose your definitions carefully
- Use total numbers rather than proportions (or whichever way suits your argument)
- Don’t provide any relevant context
- Exaggerate the importance of a possibly illusory change
- Prematurely announce the success of a policy initiative using unofficial selected data
- If all else fails, just make the numbers up
To be continued...
Submitted by Bill Peterson
Statistical reasoning in journalism education
Tests for gerrymandering
Let math save our democracy
by Sam Wang, New York Times, 5 December 2015
How gerrymandered is your Congressional district?
by Christopher Ingraham, Washington Post,15 May 2014
appeal to isoperimetric inequality: in the plane, a circle maximizes the area of a closed curve with a fixed perimeter.
How to rig an election
Economist, 25 April 2002
Subtitled: "In a normal democracy, voters choose their representatives. In America, it is rapidly becoming the other way around."
"Worst of all is the state's extraordinary 17th District, which is a crab (see chart). Though most of it lies in the western part of the state, two claws stretch out towards the eastern part to grab Democratic cities in order to make the surrounding 18th and 19th districts more reliably Republican."
"as used to be said of the old Texas 6th (which was a road from Houston to Dallas), that you could kill most of the constituents by driving down the road with the car doors open."
Diet science
Are fats unhealthy? The battle over dietary guidelines
by Aaron E. Carroll, “Upshot” blog, New York Times, 12 October 2015.
Related “Upshot”: Behind new dietary guidelines, better science, February 23, 2015
Chance of gun death
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>\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.
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% |
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.”
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