108: Difference between revisions

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<div align=right>--[https://twitter.com/HillaryClinton/status/733694076961329152/photo/1?ref_src=twsrc%5Etfw tweeted] by Hiilary Clinton's campaign</div>
<div align=right>--[https://twitter.com/HillaryClinton/status/733694076961329152/photo/1?ref_src=twsrc%5Etfw tweeted] by Hiilary Clinton's campaign</div>
Suggested by Lucy Nussbaum
Suggested by Lucy Nussbaum

Revision as of 15:40, 18 July 2016

Quotations

"The p-value was never intended to be a substitute for scientific reasoning."

--Ron Wasserstein, quoted in the news release announcing the "ASA's Statement on p-Values"

Forsooth

"There are 33 percent more such women in their 20s than men. To help us see what a big difference 33 percent is, Birger invites us to imagine a late-night dorm room hangout that’s drawing to an end, and everyone wants to hook up. 'Now imagine,' he writes, that in this dorm room, 'there are three women and two men.'"

--from the New York Times Book Review

Cited in Imagine 33 percent at Jordan Ellenberg's "Quodmodocumque" blog (20 November 2015).

Submitted by Priscilla Bremser


“I showed all the data together, which helped disguise the bimodal distribution. Nothing wrong with that. All the data is there. Every piece.... [But then he suggested using] thick and thin lines to try and dress it up, or changing colors to divert attention.”

--Bob Schubert (Takata airbag engineer)

quoted in: Takata emails show brash exchanges about data tampering, New York Times, 4 January 2016

Submitted by Bill Peterson


--tweeted by Hiilary Clinton's campaign

Suggested by Lucy Nussbaum

ASA issues statement on p-values

The ASA's statement on p-values: Context, process, and purpose
by Ronald L. Wasserstein and Nicole A. Lazar, The American Statistician, 70:2 (2016).

The report, based on contributions from 26 experts, enunciates six main principles, with discussion of each. The principles are reproduced below.

  1. P-values can indicate how incompatible the data are with a specified statistical model.
  2. P-values do not measure the probability that the studied hypothesis is true, or the probability that the data were produced by random chance alone.
  3. Scientific conclusions and business or policy decisions should not be based only onwhether a p-value passes a specific threshold.
  4. Proper inference requires full reporting and transparency.
  5. A p-value, or statistical significance, does not measure the size of an effect or the importance of a result.
  6. By itself, a p-value does not provide a good measure of evidence regarding a model or hypothesis.

Violations of the second principle are common in the popular press (and even appear in published research papers), and are often presented in the Forsooth section this wiki (here is one from Chance News 105).

Even explaining the issue can be tricky. When Nature News reported that the journal Basic and Applied Social Psychology would no longer publish papers presenting P-values, they had to issue the following correction:

Clarified: This story originally asserted that “The closer to zero the P value gets, the greater the chance the null hypothesis is false.” P values do not give the probability that a null hypothesis is false, they give the probability of obtaining data at least as extreme as those observed, if the null hypothesis was true. It is by convention that smaller P values are interpreted as stronger evidence that the null hypothesis is false. The text has been changed to reflect this.

FiveThirtyEight blog claimed that Not even scientists can easily explain P-values.

The FiveThirtyEight blog developed an interactive demonstration of p-value hacking, entitled Hack Your Way To Scientific Glory. A scatterplot shows the strength of the economy vs. the degree of political power of your preferred party (choose Democrat or Republican). Your mission is to show that having your party "in control" of the government helps "the economy" by tweaking how the variables are defined.

Reproducibility crisis in psychology. Andrew Gelman, No, this post is not 30 days early: Psychological Science backs away from null hypothesis significance testing

John Oliver on scientific studies

Oliver’s rant about science reporting should be taken seriously
by John Timmer, arstechnica.com, 10 May 2016