Sandbox: Difference between revisions

From ChanceWiki
Jump to navigation Jump to search
 
(540 intermediate revisions by the same user not shown)
Line 1: Line 1:
==Bogus statistics==
[How To Spot a Bogus Statistic]<br>
by Geoffrey James, Inc.com, 30 May 2015


The article begins by citing Bill Gates recent [http://www.gatesnotes.com/About-Bill-Gates/6-Books-I-Recommended-for-TED-2015 recommendation] that everyone should read the Darrell Huff classic ''How to Lie With Statistics''. 


As an object lesson, James considers efforts to dispute the scientific consensus on anthropogenic climate change.
==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.”
<div align=right>-- Donald Kennedy (former president of Stanford University), ''Academic Duty'', Harvard University Press, 1997, p.17</div>


Submitted by Bill Peterson
----


==Predicting GOP debate participants==
"Using scientific language and measurement doesn’t prevent a researcher from conducting flawed experiments and drawing wrong conclusions — especially when they confirm preconceptions."
Ethan Brown posted this following link on the Isolated Statisticians list:


:[http://www.nytimes.com/interactive/2015/07/21/upshot/election-2015-the-first-gop-debate-and-the-role-of-chance.html The first G.O.P. debate: Who’s in, who’s out and the role of chance]<br>
<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>
:by Kevin Quealy and Amanda Cox , "Upshot" blog ''New York Times'', 21 July 2015


==Sleeping beauties==
==In progress==
Doulas Rogers sent a link to the following:
[https://www.nytimes.com/2018/11/07/magazine/placebo-effect-medicine.html What if the Placebo Effect Isn’t a Trick?]<br>
by Gary Greenberg, ''New York Times Magazine'', 7 November 2018


:[http://www.nytimes.com/2015/05/20/sports/football/nfl-explores-making-the-2-point-conversion-more-tempting.html Defining and identifying Sleeping Beauties in science]<br>
[https://www.nytimes.com/2019/07/17/opinion/pretrial-ai.html The Problems With Risk Assessment Tools]<br>
:by Qing Ke, et. al., ''PNAS'' (vol. 112 no. 24), 2015.
by Chelsea Barabas, Karthik Dinakar and Colin Doyle, ''New York Times'', 17 July 2019


[http://www.psmag.com/books-and-culture/sleeping-beauties-of-science The sleeping beauties of science]<br>
==Hurricane Maria deaths==
by Nathan Collins, ''Pacific Standard'', 28 May 2015
Laura Kapitula sent the following to the Isolated Statisticians e-mail list:


Cites a [http://www.tandfonline.com/doi/abs/10.1080/14786440109462720?journalCode=tphm17#.VagU4aYYeVg 1901 paper] by Karl Pearson
:[Why counting casualties after a hurricane is so hard]<br>
:by Jo Craven McGinty, Wall Street Journal, 7 September 2018


==Maybe the "hot hand" exists after all==
The article is subtitled: Indirect deaths—such as those caused by gaps in medication—can occur months after a storm, complicating tallies
Kevin Tenenbaum sent a link to the following working paper :
 
Laura noted that
:[http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2627354 Surprised by the gambler’s and hot hand fallacies? A truth in the law of small numbers]<br>
:[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 Joshua Miller and Adam Sanjurjo, ''Social Science Research Network'', 6 July 2015
:by Glenn Kessler, ''Washington Post'', 1 June 2018


The abstract announces, "We find a subtle but substantial bias in a standard measure of the conditional dependence of present outcomes on streaks of past outcomes in sequential data. The mechanism is driven by a form of selection bias, which leads to an underestimate of the true conditional probability of a given outcome when conditioning on prior outcomes of the same kind."  The authors give the following simple example to illustrate the bias
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.
<blockquote>
Jack takes a coin from his pocket and decides that he will flip it 4 times in a row, writing down the outcome of each flip on a scrap of paper. After he is done flipping, he will look at the flips that immediately followed an outcome of heads, and compute the relative frequency of heads on those flips. Because the coin is fair, Jack of course expects this conditional relative frequency to be equal to the probability of flipping a heads: 0.5. Shockingly, Jack is wrong. If he were to sample 1 million fair coins and flip each coin 4 times, observing the conditional relative frequency for each coin, on average the relative frequency would be approximately 0.4.
</blockquote>
This is a surprising and counterintuitive assertionTo understand what it means, consider enumerating the 16 possible equally likely sequences of four tosses 
(this is a less notation-intensive adaptation of Table 1 in the paper).
<center>
{| class="wikitable" style="text-align:center"
|-
! Sequence<br> of tosses !! Count of <br> H followed by H !! Proportion of <br> H followed by H
|-
|  TTTT || 0 out of 0 || ---
|-
| TTTH  || 0 out of 0 ||  ---
|-
| TTHT || 0 out of 1 ||  0
|-
| THTT || 0 out of 1 ||  0
|-
| HTTT || 0 out of 1 ||  0
|-
| HTTT || 0 out of 1 ||  0
|-
| TTHH || 1 out of 1 ||  1
|-
| THHT || 1 out of 2 ||  1/2
|-
| HTTH || 0 out of 1 ||  0
|-
| HTHT || 0 out of 2 ||  0
|-
| HHTT || 1 out of 2 ||  1/2
|-
| THHH || 2 out of 2 ||  1
|-
| HTHH || 1 out of 2 ||  1/2
|-
| HHTH || 1 out of 2 ||  1/2
|-
| HHHT || 2 out of 3 ||  2/3
|-
| HHHH || 3 out of 3 ||  1
|-
! TOTAL || 12 out of 24 ||
|}
</center>


In each of the 16 sequences, only the first three positions have immediate followers.  Of course among these 48 total positions, 24 are heads and 24 are tails.  For those that are heads, we count how often the following toss is also heads. The sequence TTTT has no heads in the first three positions, so there are no opportunities for a head to follow a head; we record this in the second column as 0 successes in 0 opportunities.  The same is true for TTTHThe sequence THHT has 2 heads in the first three positions; since 1 is followed by a head and 1 by a tail, we record 1 success in 2 opportunities. Summing successes and opportunities for this column gives 12 out of 24, which is no surprise: a head is equally likely to be followed by a head or by a tail. So far nothing is unusual here. This property of independent tosses is often cited as evidence against the hot-hand phenomenon.
President Trump has asserted that the actual number is
[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 tollThat work is not complete.
[https://prstudy.publichealth.gwu.edu/ George Washington University study]


But now Miller and Sanjurjo point out the the selection bias inherent in observing a finite sequence after it has been generated: the first flip in a streak of heads will not figure in the proportion of heads that follow a head. Since the overall proportion of heads in the sequence is 1/2, the proportion of heads that follow a head is necessarily less than 1/2. The third column computes for each sequence the relative frequency of a head following a head.  This is what the paper calls a "conditional relative frequency", denoted <math>\,\hat{p}(H|H)</math>.  The first two sequences do not provide values.  Averaging over the 14 remaining (equally likely) sequences gives  (17/3)/14 &asymp; 0.4048.  This calculation underlies comment above that "the relative frequency would be approximately 0.4".
:[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


Again quoting from the paper "The implications for learning are stark: so long as decision makers experience finite length sequences, and simply observe the relative frequencies of one outcome when conditioning on previous outcomes in each sequence, they will never unlearn a belief in the gambler's fallacy."
----
[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]


(See also [https://www.causeweb.org/wiki/chance/index.php/Chance_News_101#The_hot_hand.2C_revisited The hot hand revisited] in Chance News 101 for some earlier data analyses asserting evidence for the hot hand phenomenon.)
[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.]


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


==Accidental insights==
==Accidental insights==
Line 141: Line 119:


----
----
==The p-value ban==
http://www.statslife.org.uk/opinion/2114-journal-s-ban-on-null-hypothesis-significance-testing-reactions-from-the-statistical-arena

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