Author Archives: Mayo

The First Eye-Opener: Error Probing Tools vs Logics of Evidence (Excursion 1 Tour II)

1.4, 1.5

In Tour II of this first Excursion of Statistical Inference as Severe Testing: How to Get Beyond the Statistics Wars (SIST, 2018, CUP),  I pull back the cover on disagreements between experts charged with restoring integrity to today’s statistical practice. Some advised me to wait until later (in the book) to get to this eye-opener. Granted, the full story involves some technical issues, but after many months, I think I arrived at a way to get to the heart of things informally (with a promise of more detailed retracing of steps later on). It was too important not to reveal right away that some of the most popular “reforms” fall down on the job even with respect to our most minimal principle of evidence (you don’t have evidence for a claim if little if anything has been done to probe the ways it can be flawed).  Continue reading

Categories: Error Statistics, law of likelihood, SIST | 14 Comments

The Current State of Play in Statistical Foundations: A View From a Hot-Air Balloon

1.3

Continue to the third, and last stop of Excursion 1 Tour I of Statistical Inference as Severe Testing: How to Get Beyond the Statistics Wars (2018, CUP)–Section 1.3. It would be of interest to ponder if (and how) the current state of play in the stat wars has shifted in just one year. I’ll do so in the comments. Use that space to ask me any questions.

How can a discipline, central to science and to critical thinking, have two methodologies, two logics, two approaches that frequently give substantively different answers to the same problems? … Is complacency in the face of contradiction acceptable for a central discipline of science? (Donald Fraser 2011, p. 329)

We [statisticians] are not blameless … we have not made a concerted professional effort to provide the scientific world with a unified testing methodology. (J. Berger 2003, p. 4)

Continue reading

Categories: Statistical Inference as Severe Testing | 3 Comments

Severity: Strong vs Weak (Excursion 1 continues)

1.2

Marking one year since the appearance of my book: Statistical Inference as Severe Testing: How to Get Beyond the Statistics Wars (2018, CUP), let’s continue to the second stop (1.2) of Excursion 1 Tour 1. It begins on p. 13 with a quote from statistician George Barnard. Assorted reflections will be given in the comments. Ask me any questions pertaining to the Tour.

 

  • 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)

Continue reading

Categories: Statistical Inference as Severe Testing | 5 Comments

How My Book Begins: Beyond Probabilism and Performance: Severity Requirement

This week marks one year since the general availability of my book: Statistical Inference as Severe Testing: How to Get Beyond the Statistics Wars (2018, CUP). Here’s how it begins (Excursion 1 Tour 1 (1.1)). Material from the preface is here. I will sporadically give some “one year later” reflections in the comments. I invite readers to ask me any questions pertaining to the Tour.

The journey begins..(1.1)

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

Categories: Statistical Inference as Severe Testing, Statistics | 4 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) 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

Gelman blogged our exchange on abandoning statistical significance

A. Gelman

I came across this post on Gelman’s blog today:

Exchange with Deborah Mayo on abandoning statistical significance

It was straight out of blog comments and email correspondence back when the ASA, and significant others, were rising up against the concept of statistical significance. Here it is: Continue reading

Categories: Gelman blogs an exchange with Mayo | Tags: | 7 Comments

All She Wrote (so far): Error Statistics Philosophy: 8 years on

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Error Statistics Philosophy: Blog Contents (8 years)
By: D. G. Mayo

Dear Reader: I began this blog 8 years ago (Sept. 3, 2011)! A double celebration is taking place at the Elbar Room Friday evening (a smaller one was held earlier in the week), both for the blog and the 1 year anniversary of the physical appearance of my book: Statistical Inference as Severe Testing: How to Get Beyond the Statistics Wars [SIST] (CUP). A special rush edition made an appearance on Sept 3, 2018 in time for the RSS meeting in Cardiff. If you’re in the neighborhood, stop by for some Elba Grease.

Ship Statinfasst made its most recent journey at the Summer Seminar for Phil Stat from July 28-Aug 11, co-directed with Aris Spanos. It was one of the main events that occupied my time the past academic year, from the planning, advertising and running. We had 15 fantastic faculty and post-doc participants (from 55 applicants), and plan to continue the movement to incorporate PhilStat in philosophy and methodology, both in teaching and research. You can find slides from the Seminar (zoom videos, including those of special invited speakers, to come) on SummerSeminarPhilStat.com. Slides and other materials from the Spring Seminar co-taught with Aris Spanos (and cross-listed with Economics) can be found on this blog here

Continue reading

Categories: 8 year memory lane, blog contents, Metablog | 3 Comments

(one year ago) RSS 2018 – Significance Tests: Rethinking the Controversy

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Here’s what I posted 1 year ago on Aug 30, 2018.

 

Day 2, Wednesday 05/09/2018

11:20 – 13:20

Keynote 4 – Significance Tests: Rethinking the Controversy Assembly Room

Speakers:
Sir David Cox, Nuffield College, Oxford
Deborah Mayo, Virginia Tech
Richard Morey, Cardiff University
Aris Spanos, Virginia Tech

Intermingled in today’s statistical controversies are some long-standing, but unresolved, disagreements on the nature and principles of statistical methods and the roles for probability in statistical inference and modelling. In reaction to the so-called “replication crisis” in the sciences, some reformers suggest significance tests as a major culprit. To understand the ramifications of the proposed reforms, there is a pressing need for a deeper understanding of the source of the problems in the sciences and a balanced critique of the alternative methods being proposed to supplant significance tests. In this session speakers offer perspectives on significance tests from statistical science, econometrics, experimental psychology and philosophy of science. There will be also be panel discussion.

Categories: memory lane | Tags: | Leave a comment

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] 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, 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

A. Spanos: Egon Pearson’s Neglected Contributions to Statistics

Continuing with posts on E.S. Pearson in marking his birthday:

Egon Pearson’s Neglected Contributions to Statistics

by Aris Spanos

    Egon Pearson (11 August 1895 – 12 June 1980), is widely known today for his contribution in recasting of Fisher’s significance testing into the Neyman-Pearson (1933) theory of hypothesis testing. Occasionally, he is also credited with contributions in promoting statistical methods in industry and in the history of modern statistics; see Bartlett (1981). What is rarely mentioned is Egon’s early pioneering work on:

(i) specification: the need to state explicitly the inductive premises of one’s inferences,

(ii) robustness: evaluating the ‘sensitivity’ of inferential procedures to departures from the Normality assumption, as well as

(iii) Mis-Specification (M-S) testing: probing for potential departures from the Normality  assumption.

Arguably, modern frequentist inference began with the development of various finite sample inference procedures, initially by William Gosset (1908) [of the Student’s t fame] and then Fisher (1915, 1921, 1922a-b). These inference procedures revolved around a particular statistical model, known today as the simple Normal model:

Xk ∽ NIID(μ,σ²), k=1,2,…,n,…             (1)

where ‘NIID(μ,σ²)’ stands for ‘Normal, Independent and Identically Distributed with mean μ and variance σ²’. These procedures include the ‘optimal’ estimators of μ and σ², Xbar and s², and the pivotal quantities:

(a) τ(X) =[√n(Xbar- μ)/s] ∽ St(n-1),  (2)

(b) v(X) =[(n-1)s²/σ²] ∽ χ²(n-1),        (3)

where St(n-1) and χ²(n-1) denote the Student’s t and chi-square distributions with (n-1) degrees of freedom. Continue reading

Categories: Egon Pearson, Statistics | Leave a comment

Statistical Concepts in Their Relation to Reality–E.S. Pearson

11 August 1895 – 12 June 1980

In marking Egon Pearson’s birthday (Aug. 11), I’ll  post some Pearson items this week. They will contain some new reflections on older Pearson posts on this blog. Today, I’m posting “Statistical Concepts in Their Relation to Reality” (Pearson 1955). I’ve linked to it several times over the years, but always find a new gem or two, despite its being so short. E. Pearson rejected some of the familiar tenets that have come to be associated with Neyman and Pearson (N-P) statistical tests, notably the idea that the essential justification for tests resides in a long-run control of rates of erroneous interpretations–what he termed the “behavioral” rationale of tests. In an unpublished letter E. Pearson wrote to Birnbaum (1974), he talks about N-P theory admitting of two interpretations: behavioral and evidential:

“I think you will pick up here and there in my own papers signs of evidentiality, and you can say now that we or I should have stated clearly the difference between the behavioral and evidential interpretations. Certainly we have suffered since in the way the people have concentrated (to an absurd extent often) on behavioral interpretations”.

(Nowadays, it might be said that some people concentrate to an absurd extent on “science-wise error rates” in their view of statistical tests as dichotomous screening devices.) Continue reading

Categories: Egon Pearson, phil/history of stat, Philosophy of Statistics | Tags: , , | Leave a comment

Performance or Probativeness? E.S. Pearson’s Statistical Philosophy: Belated Birthday Wish

E.S. Pearson

This is a belated birthday post for E.S. Pearson (11 August 1895-12 June, 1980). It’s basically a post from 2012 which concerns an issue of interpretation (long-run performance vs probativeness) that’s badly confused these days. I’ll post some Pearson items this week to mark his birthday.

HAPPY BELATED BIRTHDAY EGON!

Are methods based on error probabilities of use mainly to supply procedures which will not err too frequently in some long run? (performance). Or is it the other way round: that the control of long run error properties are of crucial importance for probing the causes of the data at hand? (probativeness). I say no to the former and yes to the latter. This, I think, was also the view of Egon Sharpe (E.S.) Pearson. 

Cases of Type A and Type B

“How far then, can one go in giving precision to a philosophy of statistical inference?” (Pearson 1947, 172)

Pearson considers the rationale that might be given to N-P tests in two types of cases, A and B:

“(A) At one extreme we have the case where repeated decisions must be made on results obtained from some routine procedure…

(B) At the other is the situation where statistical tools are applied to an isolated investigation of considerable importance…?” (ibid., 170)

Continue reading

Categories: E.S. Pearson, Error Statistics | Leave a comment

S. Senn: Red herrings and the art of cause fishing: Lord’s Paradox revisited (Guest post)

 

Stephen Senn
Consultant Statistician
Edinburgh

Background

Previous posts[a],[b],[c] of mine have considered Lord’s Paradox. To recap, this was considered in the form described by Wainer and Brown[1], in turn based on Lord’s original formulation:

A large university is interested in investigating the effects on the students of the diet provided in the university dining halls : : : . Various types of data are gathered. In particular, the weight of each student at the time of his arrival in September and his weight the following June are recorded. [2](p. 304)

The issue is whether the appropriate analysis should be based on change-scores (weight in June minus weight in September), as proposed by a first statistician (whom I called John) or analysis of covariance (ANCOVA), using the September weight as a covariate, as proposed by a second statistician (whom I called Jane). There was a difference in mean weight between halls at the time of arrival in September (baseline) and this difference turned out to be identical to the difference in June (outcome). It thus follows that, since the analysis of change score is algebraically equivalent to correcting the difference between halls at outcome by the difference between halls at baseline, the analysis of change scores returns an estimate of zero. The conclusion is thus, there being no difference between diets, diet has no effect. Continue reading

Categories: Stephen Senn | 24 Comments

Summer Seminar in PhilStat Participants and Special Invited Speakers

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Participants in the 2019 Summer Seminar in Philosophy of Statistics

Continue reading

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The NEJM Issues New Guidelines on Statistical Reporting: Is the ASA P-Value Project Backfiring? (i)

The New England Journal of Medicine NEJM announced new guidelines for authors for statistical reporting  yesterday*. The ASA describes the change as “in response to the ASA Statement on P-values and Statistical Significance and subsequent The American Statistician special issue on statistical inference” (ASA I and II,(note) in my abbreviation). If so, it seems to have backfired. I don’t know all the differences in the new guidelines, but those explicitly noted appear to me to move in the reverse direction from where the ASA I and II(note) guidelines were heading.

The most notable point is that the NEJM highlights the need for error control, especially for constraining the Type I error probability, and pays a lot of attention to adjusting P-values for multiple testing and post hoc subgroups. ASA I included an important principle (#4) that P-values are altered and may be invalidated by multiple testing, but they do not call for adjustments for multiplicity, nor do I find a discussion of Type I or II error probabilities in the ASA documents. NEJM gives strict requirements for controlling family-wise error rate or false discovery rates (understood as the Benjamini and Hochberg frequentist adjustments). Continue reading

Categories: ASA Guide to P-values | 23 Comments

B. Haig: The ASA’s 2019 update on P-values and significance (ASA II)(Guest Post)

Brian Haig, Professor Emeritus
Department of Psychology
University of Canterbury
Christchurch, New Zealand

The American Statistical Association’s (ASA) recent effort to advise the statistical and scientific communities on how they should think about statistics in research is ambitious in scope. It is concerned with an initial attempt to depict what empirical research might look like in “a world beyond p<0.05” (The American Statistician, 2019, 73, S1,1-401). Quite surprisingly, the main recommendation of the lead editorial article in the Special Issue of The American Statistician devoted to this topic (Wasserstein, Schirm, & Lazar, 2019; hereafter, ASA II(note)) is that “it is time to stop using the term ‘statistically significant’ entirely”. (p.2) ASA II(note) acknowledges the controversial nature of this directive and anticipates that it will be subject to critical examination. Indeed, in a recent post, Deborah Mayo began her evaluation of ASA II(note) by making constructive amendments to three recommendations that appear early in the document (‘Error Statistics Philosophy’, June 17, 2019). These amendments have received numerous endorsements, and I record mine here. In this short commentary, I briefly state a number of general reservations that I have about ASA II(note). Continue reading

Categories: ASA Guide to P-values, Brian Haig | Tags: | 32 Comments

SIST: All Excerpts and Mementos: May 2018-July 2019 (updated)

Introduction & Overview

The Meaning of My Title: Statistical Inference as Severe Testing: How to Get Beyond the Statistics Wars* 05/19/18

Blurbs of 16 Tours: Statistical Inference as Severe Testing: How to Get Beyond the Statistics Wars (SIST) 03/05/19

 

Excursion 1

EXCERPTS

Tour I Ex1 TI (full proofs)

Excursion 1 Tour I: Beyond Probabilism and Performance: Severity Requirement (1.1) 09/08/18

Excursion 1 Tour I (2nd stop): Probabilism, Performance, and Probativeness (1.2) 09/11/18

Excursion 1 Tour I (3rd stop): The Current State of Play in Statistical Foundations: A View From a Hot-Air Balloon (1.3) 09/15/18

Tour II

Excursion 1 Tour II: Error Probing Tools versus Logics of Evidence-Excerpt 04/04/19

Souvenir C: A Severe Tester’s Translation Guide (Excursion 1 Tour II) 11/08/18

MEMENTOS

Tour Guide Mementos (Excursion 1 Tour II of How to Get Beyond the Statistics Wars) 10/29/18

 

Excursion 2

EXCERPTS

Tour I

Excursion 2: Taboos of Induction and Falsification: Tour I (first stop) 09/29/18

“It should never be true, though it is still often said, that the conclusions are no more accurate than the data on which they are based” (Keepsake by Fisher, 2.1) 10/05/18

Tour II

Excursion 2 Tour II (3rd stop): Falsification, Pseudoscience, Induction (2.3) 10/10/18

MEMENTOS

Tour Guide Mementos and Quiz 2.1 (Excursion 2 Tour I Induction and Confirmation) 11/14/18

Mementos for Excursion 2 Tour II Falsification, Pseudoscience, Induction 11/17/18

 

Excursion 3

EXCERPTS

Tour I

Where are Fisher, Neyman, Pearson in 1919? Opening of Excursion 3 11/30/18

Neyman-Pearson Tests: An Episode in Anglo-Polish Collaboration: Excerpt from Excursion 3 (3.2) 12/01/18

First Look at N-P Methods as Severe Tests: Water plant accident [Exhibit (i) from Excursion 3] 12/04/18

Tour II

It’s the Methods, Stupid: Excerpt from Excursion 3 Tour II (Mayo 2018, CUP) 12/11/18

60 Years of Cox’s (1958) Chestnut: Excerpt from Excursion 3 tour II. 12/29/18

Tour III

Capability and Severity: Deeper Concepts: Excerpts From Excursion 3 Tour III 12/20/18

MEMENTOS

Memento & Quiz (on SEV): Excursion 3, Tour I 12/08/18

Mementos for “It’s the Methods, Stupid!” Excursion 3 Tour II (3.4-3.6) 12/13/18

Tour Guide Mementos From Excursion 3 Tour III: Capability and Severity: Deeper Concepts 12/26/18

 

Excursion 4

EXCERPTS

Tour I

Excerpt from Excursion 4 Tour I: The Myth of “The Myth of Objectivity” (Mayo 2018, CUP) 12/26/18

Tour II

Excerpt from Excursion 4 Tour II: 4.4 “Do P-Values Exaggerate the Evidence?” 01/10/19
(Full Excursion 4 Tour II)

Tour III
(Full proofs of Excursion 4 Tour III)

Tour IV

Excerpt from Excursion 4 Tour IV: More Auditing: Objectivity and Model Checking 01/27/19

MEMENTOS

Mementos from Excursion 4: Blurbs of Tours I-IV 01/13/19

 

Excursion 5

Tour I

(Full) Excerpt: Excursion 5 Tour I — Power: Pre-data and Post-data (from “SIST: How to Get Beyond the Stat Wars”) 04/27/19

Tour II

(Full) Excerpt. Excursion 5 Tour II: How Not to Corrupt Power (Power Taboos, Retro Power, and Shpower) 06/07/19

Tour III

Deconstructing the Fisher-Neyman conflict wearing Fiducial glasses + Excerpt 5.8 from SIST 02/23/19

 

Excursion 6

Tour I Ex6 TI What Ever Happened to Bayesian Foundations?

Tour II

Excerpts: Souvenir Z: Understanding Tribal Warfare +  6.7 Farewell Keepsake from SIST + List of Souvenirs 05/04/19

 

*Statistical Inference as Severe Testing: How to Get Beyond the Statistics Wars (Mayo, CUP 2018).

Categories: SIST, Statistical Inference as Severe Testing | 2 Comments

The Statistics Wars: Errors and Casualties

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Had I been scheduled to speak later at the 12th MuST Conference & 3rd Workshop “Perspectives on Scientific Error” in Munich, rather than on day 1, I could have (constructively) illustrated some of the errors and casualties by reference to a few of the conference papers that discussed significance tests. (Most gave illuminating discussions of such topics as replication research, the biases that discredit meta-analysis, statistics in the law, formal epistemology [i]). My slides follow my abstract. Continue reading

Categories: slides, stat wars and their casualties | Tags: | Leave a comment

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