P-values

Statistics and the Higgs Discovery: 9 yr Memory Lane

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I’m reblogging two of my Higgs posts at the 9th anniversary of the 2012 discovery. (The first was in this post.) The following, was originally “Higgs Analysis and Statistical Flukes: part 2” (from March, 2013).[1]

Some people say to me: “severe testing is fine for ‘sexy science’ like in high energy physics (HEP)”–as if their statistical inferences are radically different. But I maintain that this is the mode by which data are used in “uncertain” reasoning across the entire landscape of science and day-to-day learning, at least, when we’re trying to find things out [2] Even with high level theories, the particular problems of learning from data are tackled piecemeal, in local inferences that afford error control. Granted, this statistical philosophy differs importantly from those that view the task as assigning comparative (or absolute) degrees-of-support/belief/plausibility to propositions, models, or theories.

The Higgs discussion finds its way into Tour III in Excursion 3 of my Statistical Inference as Severe Testing: How to Get Beyond the Statistics Wars (2018, CUP). You can read it (in proof form) here, pp. 202-217. in a section with the provocative title:

3.8 The Probability Our Results Are Statistical Fluctuations: Higgs’ Discovery

Continue reading

Categories: Higgs, highly probable vs highly probed, P-values | Leave a comment

Reminder: March 25 “How Should Applied Science Journal Editors Deal With Statistical Controversies?” (Mark Burgman)

The seventh meeting of our Phil Stat Forum*:

The Statistics Wars
and Their Casualties

25 March, 2021

TIME: 15:00-16:45 (London); 11:00-12:45 (New York, NOTE TIME CHANGE TO MATCH UK TIME**)

For information about the Phil Stat Wars forum and how to join, click on this link.

How should applied science journal editors deal with statistical controversies?

Mark Burgman Continue reading

Categories: ASA Guide to P-values, confidence intervals and tests, P-values, significance tests | Tags: , | 1 Comment

March 25 “How Should Applied Science Journal Editors Deal With Statistical Controversies?” (Mark Burgman)

The seventh meeting of our Phil Stat Forum*:

The Statistics Wars
and Their Casualties

25 March, 2021

TIME: 15:00-16:45 (London); 11:00-12:45 (New York, NOTE TIME CHANGE)

For information about the Phil Stat Wars forum and how to join, click on this link.

How should applied science journal editors deal with statistical controversies?

Mark Burgman Continue reading

Categories: ASA Guide to P-values, confidence intervals and tests, P-values, significance tests | Tags: , | 1 Comment

Souvenir From the NISS Stat Debate for Users of Bayes Factors (& P-Values)

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What would I say is the most important takeaway from last week’s NISS “statistics debate” if you’re using (or contemplating using) Bayes factors (BFs)–of the sort Jim Berger recommends–as replacements for P-values? It is that J. Berger only regards the BFs as appropriate when there’s grounds for a high concentration (or spike) of probability on a sharp null hypothesis,            e.g.,H0: θ = θ0.

Thus, it is crucial to distinguish between precise hypotheses that are just stated for convenience and have no special prior believability, and precise hypotheses which do correspond to a concentration of prior belief. (J. Berger and Delampady 1987, p. 330).

Continue reading

Categories: bayes factors, Berger, P-values, S. Senn | 4 Comments

My Responses (at the P-value debate)

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How did I respond to those 7 burning questions at last week’s (“P-Value”) Statistics Debate? Here’s a fairly close transcript of my (a) general answer, and (b) final remark, for each question–without the in-between responses to Jim and David. The exception is question 5 on Bayes factors, which naturally included Jim in my general answer. 

The questions with the most important consequences, I think, are questions 3 and 5. I’ll explain why I say this in the comments. Please share your thoughts. Continue reading

Categories: bayes factors, P-values, Statistics, statistics debate NISS | 1 Comment

The P-Values Debate

 

 

National Institute of Statistical Sciences (NISS): The Statistics Debate (Video)

Categories: J. Berger, P-values, statistics debate | 14 Comments

The Statistics Debate! (NISS DEBATE, October 15, Noon – 2 pm ET)

October 15, Noon – 2 pm ET (Website)

Where do YOU stand?

Given the issues surrounding the misuses and abuse of p-values, do you think p-values should be used? Continue reading

Categories: Announcement, J. Berger, P-values, Philosophy of Statistics, reproducibility, statistical significance tests, Statistics | Tags: | 9 Comments

August 6: JSM 2020 Panel on P-values & “Statistical Significance”

SLIDES FROM MY PRESENTATION

July 30 PRACTICE VIDEO for JSM talk (All materials for Practice JSM session here)

JSM 2020 Panel Flyer (PDF)
JSM online program w/panel abstract & information):

Categories: ASA Guide to P-values, Error Statistics, evidence-based policy, JSM 2020, P-values, Philosophy of Statistics, science communication, significance tests | 3 Comments

JSM 2020: P-values & “Statistical Significance”, August 6


Link: https://ww2.amstat.org/meetings/jsm/2020/onlineprogram/ActivityDetails.cfm?SessionID=219596

To register for JSM: https://ww2.amstat.org/meetings/jsm/2020/registration.cfm

Categories: JSM 2020, P-values | Leave a comment

Bad Statistics is Their Product: Fighting Fire With Fire (ii)

Mayo fights fire w/ fire

I. Doubt is Their Product is the title of a (2008) book by David Michaels, Assistant Secretary for OSHA from 2009-2017. I first mentioned it on this blog back in 2011 (“Will the Real Junk Science Please Stand Up?) The expression is from a statement by a cigarette executive (“doubt is our product”), and the book’s thesis is explained in its subtitle: How Industry’s Assault on Science Threatens Your Health. Imagine you have just picked up a book, published in 2020: Bad Statistics is Their Product. Is the author writing about how exaggerating bad statistics may serve in the interest of denying well-established risks? [Interpretation A]. Or perhaps she’s writing on how exaggerating bad statistics serves the interest of denying well-established statistical methods? [Interpretation B]. Both may result in distorting science and even in dismantling public health safeguards–especially if made the basis of evidence policies in agencies. A responsible philosopher of statistics should care. Continue reading

Categories: ASA Guide to P-values, Error Statistics, P-values, replication research, slides | 33 Comments

My paper, “P values on Trial” is out in Harvard Data Science Review

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My new paper, “P Values on Trial: Selective Reporting of (Best Practice Guides Against) Selective Reporting” is out in Harvard Data Science Review (HDSR). HDSR describes itself as a A Microscopic, Telescopic, and Kaleidoscopic View of Data Science. The editor-in-chief is Xiao-li Meng, a statistician at Harvard. He writes a short blurb on each article in his opening editorial of the issue. Continue reading

Categories: multiple testing, P-values, significance tests, Statistics | 29 Comments

The NAS fixes its (main) mistake in defining P-values!

Mayo new elbow

(reasonably) satisfied

Remember when I wrote to the National Academy of Science (NAS) in September pointing out mistaken definitions of P-values in their document on Reproducibility and Replicability in Science? (see my 9/30/19 post). I’d given up on their taking any action, but yesterday I received a letter from the NAS Senior Program officer:

Dear Dr. Mayo,

I am writing to let you know that the Reproducibility and Replicability in Science report has been updated in response to the issues that you have raised.
Two footnotes, on pages 31 35 and 221, highlight the changes. The updated report is available from the following link: NEW 2020 NAS DOC

Thank you for taking the time to reach out to me and to Dr. Fineberg and letting us know about your concerns.
With kind regards and wishes of a happy 2020,
Jenny Heimberg
Jennifer Heimberg, Ph.D.
Senior Program Officer

The National Academies of Sciences, Engineering, and Medicine

Continue reading

Categories: NAS, P-values | 2 Comments

P-Value Statements and Their Unintended(?) Consequences: The June 2019 ASA President’s Corner (b)

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Mayo writing to Kafadar

I never met Karen Kafadar, the 2019 President of the American Statistical Association (ASA), but the other day I wrote to her in response to a call in her extremely interesting June 2019 President’s Corner: “Statistics and Unintended Consequences“:

  • “I welcome your suggestions for how we can communicate the importance of statistical inference and the proper interpretation of p-values to our scientific partners and science journal editors in a way they will understand and appreciate and can use with confidence and comfort—before they change their policies and abandon statistics altogether.”

I only recently came across her call, and I will share my letter below. First, here are some excerpts from her June President’s Corner (her December report is due any day). Continue reading

Categories: ASA Guide to P-values, Bayesian/frequentist, P-values | 3 Comments

On Some Self-Defeating Aspects of the ASA’s (2019) Recommendations on Statistical Significance Tests (ii)

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“Before we stood on the edge of the precipice, now we have taken a great step forward”

 

What’s self-defeating about pursuing statistical reforms in the manner taken by the American Statistical Association (ASA) in 2019? In case you’re not up on the latest in significance testing wars, the 2016 ASA Statement on P-Values and Statistical Significance, ASA I, arguably, was a reasonably consensual statement on the need to avoid some well-known abuses of P-values–notably if you compute P-values, ignoring selective reporting, multiple testing, or stopping when the data look good, the computed P-value will be invalid. (Principle 4, ASA I) But then Ron Wasserstein, executive director of the ASA, and co-editors, decided they weren’t happy with their own 2016 statement because it “stopped just short of recommending that declarations of ‘statistical significance’ be abandoned” altogether. In their new statement–ASA II(note)–they announced: “We take that step here….Statistically significant –don’t say it and don’t use it”.

Why do I say it is a mis-take to have taken the supposed next “great step forward”? Why do I count it as unsuccessful as a piece of statistical science policy? In what ways does it make the situation worse? Let me count the ways. The first is in this post. Others will come in following posts, until I become too disconsolate to continue.[i] Continue reading

Categories: P-values, stat wars and their casualties, statistical significance tests | 14 Comments

National Academies of Science: Please Correct Your Definitions of P-values

Mayo banging head

If you were on a committee to highlight issues surrounding P-values and replication, what’s the first definition you would check? Yes, exactly. Apparently, when it came to the recently released National Academies of Science “Consensus Study” Reproducibility and Replicability in Science 2019, no one did. Continue reading

Categories: ASA Guide to P-values, Error Statistics, P-values | 20 Comments

Hardwicke and Ioannidis, Gelman, and Mayo: P-values: Petitions, Practice, and Perils (and a question for readers)

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The October 2019 issue of the European Journal of Clinical Investigations came out today. It includes the PERSPECTIVE article by Tom Hardwicke and John Ioannidis, an invited editorial by Gelman and one by me:

Petitions in scientific argumentation: Dissecting the request to retire statistical significance, by Tom Hardwicke and John Ioannidis

When we make recommendations for scientific practice, we are (at best) acting as social scientists, by Andrew Gelman

P-value thresholds: Forfeit at your peril, by Deborah Mayo

I blogged excerpts from my preprint, and some related posts, here.

All agree to the disagreement on the statistical and metastatistical issues: Continue reading

Categories: ASA Guide to P-values, P-values, stat wars and their casualties | 16 Comments

(Excerpts from) ‘P-Value Thresholds: Forfeit at Your Peril’ (free access)

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A key recognition among those who write on the statistical crisis in science is that the pressure to publish attention-getting articles can incentivize researchers to produce eye-catching but inadequately scrutinized claims. We may see much the same sensationalism in broadcasting metastatistical research, especially if it takes the form of scapegoating or banning statistical significance. A lot of excitement was generated recently when Ron Wasserstein, Executive Director of the American Statistical Association (ASA), and co-editors A. Schirm and N. Lazar, updated(note) the 2016 ASA Statement on P-Values and Statistical Significance (ASA I). In their 2019 interpretation, ASA I “stopped just short of recommending that declarations of ‘statistical significance’ be abandoned,” and in their new statement (ASA II)(note) announced: “We take that step here….’statistically significant’ –don’t say it and don’t use it”. To herald the ASA II(note), and the special issue “Moving to a world beyond ‘p < 0.05’”, the journal Nature requisitioned a commentary from Amrhein, Greenland and McShane “Retire Statistical Significance” (AGM). With over 800 signatories, the commentary received the imposing title “Scientists rise up against significance tests”! Continue reading

Categories: ASA Guide to P-values, P-values, stat wars and their casualties | 6 Comments

Palavering about Palavering about P-values

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Nathan Schachtman (who was a special invited speaker at our recent Summer Seminar in Phil Stat) put up a post on his law blog the other day (“Palavering About P-values”) on an article by a statistics professor at Stanford, Helena Kraemer. “Palavering” is an interesting word choice of Schachtman’s. Its range of meanings is relevant here [i]; in my title, I intend both, in turn. You can read Schachtman’s full post here, it begins like this:

The American Statistical Association’s most recent confused and confusing communication about statistical significance testing has given rise to great mischief in the world of science and science publishing.[ASA II 2019]note Take for instance last week’s opinion piece about “Is It Time to Ban the P Value?” Please.

Admittedly, their recent statement, which I refer to as ASA II,note has seemed to open the floodgates to some very zany remarks about P-values, their meaning and role in statistical testing. Continuing with Schachtman’s post: Continue reading

Categories: ASA Guide to P-values, P-values | Tags: | 12 Comments

Diary For Statistical War Correspondents on the Latest Ban on Speech

When science writers, especially “statistical war correspondents”, contact you to weigh in on some article, they may talk to you until they get something spicy, and then they may or may not include the background context. So a few writers contacted me this past week regarding this article (“Retire Statistical Significance”)–a teaser, I now suppose, to advertise the ASA collection(note) growing out of that conference “A world beyond P ≤ .05” way back in Oct 2017, where I gave a paper*. I jotted down some points, since Richard Harris from NPR needed them immediately, and I had just gotten off a plane when he emailed. He let me follow up with him, which is rare and greatly appreciated. So I streamlined the first set of points, and dropped any points he deemed technical. I sketched the third set for a couple of other journals who contacted me, who may or may not use them. Here’s Harris’ article, which includes a couple of my remarks. Continue reading

Categories: ASA Guide to P-values, P-values | 42 Comments

A letter in response to the ASA’s Statement on p-Values by Ionides, Giessing, Ritov and Page

I came across an interesting letter in response to the ASA’s Statement on p-values that I hadn’t seen before. It’s by Ionides, Giessing, Ritov and Page, and it’s very much worth reading. I make some comments below. Continue reading

Categories: ASA Guide to P-values, P-values | 7 Comments

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