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My “April 1” posts for the past 8 years have been so close to the truth or possible truth that they weren’t always spotted as April Fool’s pranks, which is what made them genuine April Fool’s pranks. (After a few days I either labeled them as such, e.g., “check date!”, or revealed it in a comment). Given the level of current chaos and stress, I decided against putting up a planned post for today, so I’m just doing a memory lane of past posts. (You can tell from reading the comments which had most people fooled.)
4/1/12 Philosophy of Statistics: Retraction Watch, Vol. 1, No. 1
This morning I received a paper I have been asked to review (anonymously as is typical). It is to head up a forthcoming issue of a new journal called Philosophy of Statistics: Retraction Watch. This is the first I’ve heard of the journal, and I plan to recommend they publish the piece, conditional on revisions. I thought I would post the abstract here. It’s that interesting.
“Some Slightly More Realistic Self-Criticism in Recent Work in Philosophy of Statistics,” Philosophy of Statistics: Retraction Watch, Vol. 1, No. 1 (2012), pp. 1-19.In this paper we delineate some serious blunders that we and others have made in published work on frequentist statistical methods. First, although we have claimed repeatedly that a core thesis of the frequentist testing approach is that a hypothesis may be rejected with increasing confidence as the power of the test increases, we now see that this is completely backwards, and we regret that we have never addressed, or even fully read, the corrections found in Deborah Mayo’s work since at least 1983, and likely even before that.
You can read the rest here.
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4/1/13 Flawed Science and Stapel: Priming for a Backlash?
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Deiderik Stapel is back in the news, given the availability of the English translation of the Tilberg (Levelt and Noort Committees) Report as well as his book, Ontsporing (Dutch for “Off the Rails”), where he tries to explain his fraud. An earlier post on him is here. While the disgraced social psychologist was shown to have fabricated the data for something like 50 papers, it seems that some people think he deserves a second chance. A childhood friend, Simon Kuper, in an article “The Sin of Bad Science,” describes a phone conversation with Stapel:…..
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4/1/14 Skeptical and enthusiastic Bayesian priors for beliefs about insane asylum renovations at Dept of Homeland Security: I’m skeptical and unenthusiastic
Danver State Hospital
I had heard of medical designs that employ individuals who supply Bayesian subjective priors that are deemed either “enthusiastic” or “skeptical” as regards the probable value of medical treatments.[i] …But I’d never heard of these Bayesian designs in relation to decisions about building security or renovations! Listen to this….
You may have heard that the Department of Homeland Security (DHS), whose 240,000 employees are scattered among 50 office locations around D.C.,has been planning to have headquarters built at an abandoned insane asylum St Elizabeths in DC [ii]. (Here’s a 2015 update.)
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4/01/15 Are scientists really ready for ‘retraction offsets’ to advance ‘aggregate reproducibility’? (let alone ‘precautionary withdrawals’)
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Given recent evidence of the irreproducibility of a surprising number of published scientific findings, the White House’s Office of Science and Technology Policy (OSTP) sought ideas for “leveraging its role as a significant funder of scientific research to most effectively address the problem”, and announced funding for projects to “reset the self-corrective process of scientific inquiry”. (first noted in this post.)
You can read the rest here.
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4/1/16 Er, about those “other statistical approaches”: Hold off until a balanced critique is in?
I could have told them that the degree of accordance enabling the “6 principles” on p-values was unlikely to be replicated when it came to most of the “other approaches” with which some would supplement or replace significance tests– notably Bayesian updating, Bayes factors, or likelihood ratios (confidence intervals are dual to hypotheses tests). [My commentary is here.] So now they may be advising a “hold off” or “go slow” approach until some consilience is achieved. Is that it? I don’t know. I was tweeted an article about the background chatter taking place behind the scenes; I wasn’t one of people interviewed for this. Here are some excerpts, I may add more later after it has had time to sink in. (check back later)
You can read the rest here.
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4/1/17 and 4/1/18 were slight updates of 4/1/16. @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@
4/1/19 there’s a man at the wheel in your brain & he’s telling you what you’re allowed to say (not probability, not likelihood)
It seems like every week something of excitement in statistics comes down the pike. Last week I was contacted by Richard Harris (and 2 others) about the recommendation to stop saying the data reach “significance level p” but rather simply say
“the p-value is p”.
(For links, see my previous post.) Friday, he wrote to ask if I would comment on a proposed restriction (?) on saying a test had high power! I agreed that we shouldn’t say a test has high power, but only that it has a high power to detect a specific alternative, but I wasn’t aware of any rulings from those in power on power. He explained it was an upshot of a reexamination by a joint group of the boards of statistical associations in the U.S. and UK. of the full panoply of statistical terms. Something like that. I agreed to speak with him yesterday. He emailed me the proposed ruling on power:
You can read the rest here.
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Error Statistics Philosophy 