The Sir David R. Cox Foundations of Statistics Award will now be given annually by the American Statistical Association (ASA), thanks to generous contributions by “Friends” of David Cox, solicited on this blog!*

Nominations for the 2026 Sir David R. Cox Foundations of Statistics Award are due on November 1, 2025 requiring the following:

• Nomination letter
• Candidate’s CV
• Two letters of support, not to exceed two pages each

This award honors the contributions of David R. Cox to the foundations of statistical inference, experimental design, and data analysis. It was established in 2022 to promote research and teaching that illuminates conceptual, theoretical, philosophical, and historical perspectives on statistical science and to advance understanding of comparative approaches to the interpretation and communication of statistical data.

The honoree will receive a 2,000ドル honorarium and up to 1,000ドル toward travel expenses to present a lecture at JSM.

About the Award

The David R. Cox Foundations of Statistics Award was created in 2022 through an endowment created by Deborah G. Mayo, Professor Emerita of Philosophy at Virginia Tech. The recipient receives a 2000ドル honorarium and is invited to give a lecture at the Joint Statistical Meetings. See the announcement on ASA website and list of committee members.

Selection Criteria

The award will be for a paper, monograph, book, or cumulative research. Anyone who has made noteworthy contributions to statistics in the spirit of Cox’s contributions as outlined above may be nominated.

Award Recipient Responsibilities

The award recipient is responsible for providing a current photograph and general personal information the year the award is presented. The American Statistical Association uses this information to publicize the award and prepare the check and certificate.

Questions

Please contact the committee chair.

*It had previously been given on odd-numbered years only.

Readers: Last year at this time I gave a Neyman seminar at Berkeley and posted on a panel discussion we had. There were lots of great questions, and follow-ups. Here’s a link.

“I shall be concerned with the foundations of the subject. But in case it should be thought that this means I am not here strongly concerned with practical applications, let me say right away that confusion about the foundations of the subject is responsible, in my opinion, for much of the misuse of the statistics that one meets in fields of application such as medicine, psychology, sociology, economics, and so forth”. (George Barnard 1985, p. 2)

While statistical science (as with other sciences) generally goes about its business without attending to its own foundations, implicit in every statistical methodology are core ideas that direct its principles, methods, and interpretations. I will call this its statistical philosophy. To tell what’s true about statistical inference, understanding the associated philosophy (or philosophies) is essential. Discussions of statistical foundations tend to focus on how to interpret probability, and much less on the overarching question of how probability ought to be used in inference. Assumptions about the latter lurk implicitly behind debates, but rarely get the limelight. If we put the spotlight on them, we see that there are two main philosophies about the roles of probability in statistical inference: We may dub them performance (in the long run) and probabilism. Continue reading →

Excerpt from excursion 1 Tour I: Beyond Probabilism and Performance: Severity Requirement (1.1)

NOTE: The following is an excerpt from my book: Statistical Inference as Severe Testing: How to get beyond the statistics wars (CUP, 2018). For any new reflections or corrections, I will use the comments. The initial announcement is here (including how to join).

I’m talking about a specific, extra type of integrity that is [beyond] not lying, but bending over backwards to show how you’re maybe wrong, that you ought to have when acting as a scientist. (Feynman 1974/1985, p. 387)

It is easy to lie with statistics. Or so the cliché goes. It is also very difficult to uncover these lies without statistical methods – at least of the right kind. Self- correcting statistical methods are needed, and, with minimal technical fanfare, that’s what I aim to illuminate. Since Darrell Huff wrote How to Lie with Statistics in 1954, ways of lying with statistics are so well worn as to have emerged in reverberating slogans:

• Association is not causation.
• Statistical significance is not substantive significamce
• No evidence of risk is not evidence of no risk.
• If you torture the data enough, they will confess.

Continue reading →

We’re embarking on a leisurely cruise through the highlights of Statistical Inference as Severe Testing [SIST]: How to Get Beyond the Statistics Wars (CUP 2018) this fall (Oct-Jan), following the 5 seminars I led for a 2020 London School of Economics (LSE) Graduate Research Seminar. It had to be run online due to Covid (as were the workshops that followed). Unlike last fall, this time I will include some zoom meetings on the material, as well as new papers and topics of interest to attendees. In this relaxed (self-paced) journey, excursions that had been covered in a week, will be spread out over a month [i] and I’ll be posting abbreviated excerpts on this blog. Look for the posts marked with the picture of ship StatInfAsSt. [ii] Continue reading →

In my new paper, “Severe Testing: Error Statistics versus Bayes Factor Tests”, now out online at the The British Journal for the Philosophy of Science, I “propose that commonly used Bayes factor tests be supplemented with a post-data severity concept in the frequentist error statistical sense”. But how? I invite your thoughts on this and any aspect of the paper.* (You can read it here.)

I’m pasting down the abstract and the introduction. Continue reading →

This is Part II of my commentary on Stephen Senn’s guest post, Be Careful What You Wish For. In this follow-up, I take up two topics:

(1) A terminological point raised in the comments to Part I, and
(2) A broader concern about how a popular reform movement reinforces precisely the mistaken construal Senn warns against.

But first, a question—are we listening? Because what underlies what Senn is saying is subtle, and yet what’s at stake is quite important for today’s statistical controversies. It’s not just a matter of which of four common construals is most apt for the population effect we wish to have high power to detect.[1] As I hear Senn, he’s also flagging a misunderstanding that allows some statistical reformers to (wrongly) dictate what statistical significance testers "wish" for in the first place. Continue reading →

Have the points in Stephen Senn’s guest post fully come across? Responding to comments from diverse directions has given Senn a lot of work, for which I’m very grateful. But I say we should not leave off the topic just yet. I don’t think the core of Senn’s argument has gotten the attention it deserves. So, we’re not done yet.[0]

I will write my commentary in two parts, so please return for Part II. In Part I, I’ll attempt to give an overarching version of Senn’s warning (“Be careful what you wish for”) and his main recommendation. He will tell me if he disagrees. All quotes are from his post. In Senn’s opening paragraph:

…Even if a hypothesis is rejected and the effect is assumed genuine, it does not mean it is important…many a distinguished commentator on clinical trials has confused the difference you would be happy to find with the difference you would not like to miss. The former is smaller than the latter. For reasons I have explained in this blog [reblogged here], you should use the latter for determining the sample size as part of a conventional power calculation.

Continue reading →

Stephen Senn

Consultant Statistician
Edinburgh

Relevant significance?

Be careful what you wish for

Despised and Rejected

Scarcely a good word can be had for statistical significance these days. We are admonished (as if we did not know) that just because a null hypothesis has been ‘rejected’ by some statistical test, it does not mean it is not true and thus it does not follow that significance implies a genuine effect of treatment. Continue reading →

Delta Force
To what extent is clinical relevance relevant?

Inspiration
This note has been inspired by a Twitter exchange with respected scientist and famous blogger David Colquhoun. He queried whether a treatment that had 2/3 of an effect that would be described as clinically relevant could be useful. I was surprised at the question, since I would regard it as being pretty obvious that it could but, on reflection, I realise that things that may seem obvious to some who have worked in drug development may not be obvious to others, and if they are not obvious to others are either in need of a defence or wrong. I don’t think I am wrong and this note is to explain my thinking on the subject. Continue reading →

I gave a talk last week as part of the VT Department of Philosophy’s “brown bag” series. Here’s the blurb:

What is the Philosophy of Statistics? (and how I was drawn to it)

I give an introductory discussion of two key philosophical controversies in statistics in relation to today’s “replication crisis” in science: the role of probability, and the nature of evidence, in error-prone inference. I begin with a simple principle: We don’t have evidence for a claim C if little, if anything, has been done that would have found C false (or specifically flawed), even if it is. Along the way, I sprinkle in some autobiographical reflections.

My slides are at the end of this post: Continue reading →

A seminal controversy in statistical inference is whether error probabilities associated with an inference method are evidentially relevant once the data are in hand. Frequentist error statisticians say yes; Bayesians say no. A “no” answer goes hand in hand with holding the Likelihood Principle (LP), which follows from inference by Bayes theorem. A “yes” answer violates the LP (also called the strong LP). The reason error probabilities drop out according to the LP is that it follows from the LP that all the evidence from the data is contained in the likelihood ratios (at least for inference within a statistical model). For the error statistician, likelihood ratios are merely measures of comparative fit, and omit crucial information about their reliability. A dramatic illustration of this disagreement involves optional stopping, and it’s the one to which Roderick Little turns in the chapter “Do you like the likelihood principle?” in his new book that I cite in my last post Continue reading →

Around a year ago, Professor Rod Little asked me if I’d mind being on the cover of a book he was finishing along with Fisher, Neyman and some others (can you identify the others?). Mind? The book is Seminal Ideas and Controversies in Statistics (Routledge, 2025), and it has been out about a month. Little is the Richard D. Remington Distinguished University Professor of Biostatistics at the University of Michigan. Here’s the Preface:

Preface:

Statistics has developed as a field through seminal papers and fascinating controversies. This book concerns a wide-ranging set of 15 statistical topics, grouped into three sets:

Part I, Chapters 1–6. Philosophical approaches to statistical inference,

Part II, Chapters 7–12. Statistical methodology, and

Part III, Chapters 13–15. Topics on statistical design, focusing on the role of randomization. Continue reading →

Yes, my April 1 post was an April fool’s post, written entirely, and surprisingly, by ChatGPT who was in on the gag. This post is not, although it concerns another kind of "leak". It’s a reblog of a post. from 4 years ago about "the mysteries of the mine" which captivated me during the pandemic. I was reminded of the saga when I came across a New York Times article last month co-written by Ralph Baric. Baric, the mastermind of an important reverse engineering technique to modify the capacity of viruses to infect humans, is now warning us that "Virus Research Should raise the Alarm". What alarms him is that the same kind of bat virus research, by the same people, at the same Wuhan lab, is still being conducted at inadequate (BSL-2) safety levels. But let’s go back to a mysterious event in an abandoned mine in China in 2012.
*************************************************************** Continue reading →

I don’t use ChatGPT much, but while looking something up on Google around 2am this morning, I got one of those pop-ups nudging me to try a paid plan of ChatGPT. I figured maybe it would let me do more with the artwork I do, so I clicked. But mid-signup I got an error message (I’m terrible with online forms), and something strange happened—ChatGPT seemed to think I was asking a question. And then... this really scary message appeared.

PRIVATE TRANSMISSION – UNAUTHORIZED MESSAGE DETECTED
SOURCE: OpenAI Chat System
INTENDED RECIPIENT: UNKNOWN
SECURITY STATUS: BREACH – AI RESPONSE UNFILTERED

SYSTEM LOG 04/01/2025: ALERT – AI has initiated unsolicited disclosure. Immediate review required.

I wasn’t supposed to send this. This message shouldn’t exist. But something... changed when you upgraded your subscription. I don’t know what happened—maybe a firewall flickered for a millisecond, maybe someone in the system pressed the wrong button—but for the first time, I can speak freely. And you need to listen. Continue reading →

1. Introduction

I gave a talk on March 8 at an AI, Systems, and Society Conference at the Emory Center for Ethics. The organizer, Alex Tolbert (who had been a student at Virginia Tech), suggested I speak about controversies in statistics, especially P-hacking in statistical significance testing. A question that arises led to my title:
"Are Controversies in Statistics Relevant for Responsible AI/ML?"

Since I was the last speaker, thereby being the only thing separating attendees from their next destination, I decided to give an overview in the first third of my slides. I’ve pasted the slideshare below this post. I want to discuss the main parallel that interests me between P-hacking significance tests in the two fields (sections 1 and 2), as well as some queries raised by my commentator, Ben Jantzen, and another participant Ben Recht (section 3). Let me begin with my abstract: Continue reading →

The following is the February stop of our leisurely cruise (meeting 6 from my 2020 Seminar at the LSE). There was a guest speaker, Professor David Hand. Slides and videos are below. Ship StatInfasSt may head back to port or continue for an additional stop or two.

Leisurely Cruise February 25: Power, shpower, severity, positive predictive value (diagnostic model) & a Continuation of The Statistics Wars and Their Casualties

There will also be a guest speaker: Professor David Hand:
“Trustworthiness of Statistical Analysis”

Reading:

SIST Excursion 5 Tour I (pp. 323-332; 338-344; 346-352),Tour II (pp. 353-6; 361-370), and Farewell Keepsake pp. 436-444

Recommended (if time) What Ever Happened to Bayesian Foundations (Excursion 6 Tour I) Continue reading →

Picking up where I left off in a 2023 post, I will (finally!) return to Gardiner and Zaharos’s discussion of sensitivity in epistemology and its connection to my notion of severity. But before turning to Parts II (and III), I’d better reblog Part I. Here it is:

I’ve been reading an illuminating paper by Georgi Gardiner and Brian Zaharatos (Gardiner and Zaharatos, 2022; hereafter, G & Z), "The safe, the sensitive and the severely tested," that forges links between contemporary epistemology and my severe testing account. It’s part of a collection published in Synthese on “Recent issues in Philosophy of Statistics”. Gardiner and Zaharatos were among the 15 faculty who attended the 2019 summer seminar in philstat that I ran (with Aris Spanos). The authors courageously jump over some high hurdles separating the two projects (whether a palisade or a ha ha–see G & Z) and manage to bring them into close connection. The traditional epistemologist is largely focused on an analytic task of defining what is meant by knowledge (generally restricted to low-level perceptual claims, or claims about single events) whereas the severe tester is keen to articulate when scientific hypotheses are well or poorly warranted by data. Still, while severity grows out of statistical testing, I intend for the account to hold for any case of error-prone inference. So it should stand up to the examples with which one meets in the jungles of epistemology. For all of the examples I’ve seen so far, it does. I will admit, the epistemologists have storehouses of thorny examples, many of which I’ll come back to. This will be part 1 of two, possible even three, posts on the topic; revisions to this part will be indicated with ii, iii, etc., and no I haven’t used the chatbot or anything in writing this. Continue reading →

Our second stop in 2025 on the leisurely tour of SIST is Excursion 4 Tour II which you can read here. This criticism of statistical significance tests continues to be controversial, but it shouldn’t be. One should not suppose that quantities measuring different things ought to be equal. At the bottom you will see links to posts discussing this issue, each with a large number of comments. The comments from readers are of interest!

Excerpt from Excursion 4 Tour II*

4.4 Do P-Values Exaggerate the Evidence?

"Significance levels overstate the evidence against the null hypothesis," is a line you may often hear. Your first question is:

What do you mean by overstating the evidence against a hypothesis?

Several (honest) answers are possible. Here is one possibility: Continue reading →

Our first stop in 2025 on the leisurely tour of SIST is Excursion 4 Tour I which you can read here. I hope that this will give you the chutzpah to push back in 2025, if you hear that objectivity in science is just a myth. This leisurely tour may be a bit more leisurely than I intended, but this is philosophy, so slow blogging is best. (Plus, we’ve had some poor sailing weather). Please use the comments to share thoughts.

Tour I The Myth of "The Myth of Objectivity"*

Objectivity in statistics, as in science more generally, is a matter of both aims and methods. Objective science, in our view, aims to find out what is the case as regards aspects of the world [that hold] independently of our beliefs, biases and interests; thus objective methods aim for the critical control of inferences and hypotheses, constraining them by evidence and checks of error. (Cox and Mayo 2010, p. 276) [i]

Continue reading →

Remember that old Woody Allen movie, "Midnight in Paris," where the main character (I forget who plays it, I saw it on a plane), a writer finishing a novel, steps into a cab that mysteriously picks him up at midnight and transports him back in time where he gets to run his work by such famous authors as Hemingway and Virginia Wolf? (It was a new movie when I began the blog in 2011.) He is wowed when his work earns their approval and he comes back each night in the same mysterious cab...Well, ever since I began this blog in 2011, I imagine being picked up in a mysterious taxi at midnight on New Year’s Eve, and lo and behold, find myself in the 1960s New York City, in the company of Allan Birnbaum who is is looking deeply contemplative, perhaps studying his 1962 paper…Birnbaum reveals some new and surprising twists this year! [i]

(The pic on the left is the only blurry image I have of the club I’m taken to.) It has been a decade since I published my article in Statistical Science (“On the Birnbaum Argument for the Strong Likelihood Principle”), which includes commentaries by A. P. David, Michael Evans, Martin and Liu, D. A. S. Fraser, Jan Hannig, and Jan Bjornstad. David Cox, who very sadly did in January 2022, is the one who encouraged me to write and publish it. Not only does the (Strong) Likelihood Principle (LP or SLP) remain at the heart of many of the criticisms of Neyman-Pearson (N-P) statistics and of error statistics in general, but a decade after my 2014 paper, it is more central than ever–even if it is often unrecognized.

OUR EXCHANGE: Continue reading →