Are P Values Error Probabilities? or, “It’s the methods, stupid!” (2nd install)

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Despite the fact that Fisherians and Neyman-Pearsonians alike regard observed significance levels, or P values, as error probabilities, we occasionally hear allegations (typically from those who are neither Fisherian nor N-P theorists) that P values are actually not error probabilities. The denials tend to go hand in hand with allegations that P values exaggerate evidence against a null hypothesis—a problem whose cure invariably invokes measures that are at odds with both Fisherian and N-P tests. The Berger and Sellke (1987) article from a recent post is a good example of this. When leading figures put forward a statement that looks to be straightforwardly statistical, others tend to simply repeat it without inquiring whether the allegation actually mixes in issues of interpretation and statistical philosophy. So I wanted to go back and look at their arguments. I will post this in installments.

1. Some assertions from Fisher, N-P, and Bayesian camps

Here are some assertions from Fisherian, Neyman-Pearsonian and Bayesian camps: (I make no attempt at uniformity in writing the “P-value”, but retain the quotes as written.)

a) From the Fisherian camp (Cox and Hinkley):

For given observations y we calculate t = tobs = t(y), say, and the level of significance pobs by

pobs = Pr(T > tobs; H0).

….Hence pobs is the probability that we would mistakenly declare there to be evidence against H0, were we to regard the data under analysis as being just decisive against H0.” (Cox and Hinkley 1974, 66).

Thus pobs would be the Type I error probability associated with the test.

b) From the Neyman-Pearson N-P camp (Lehmann and Romano):

“[I]t is good practice to determine not only whether the hypothesis is accepted or rejected at the given significance level, but also to determine the smallest significance level…at which the hypothesis would be rejected for the given observation. This number, the so-called p-value gives an idea of how strongly the data contradict the hypothesis. It also enables others to reach a verdict based on the significance level of their choice.” (Lehmann and Romano 2005, 63-4) 

Very similar quotations are easily found, and are regarded as uncontroversial—even by Bayesians whose contributions stood at the foot of Berger and Sellke’s argument that P values exaggerate the evidence against the null. Continue reading

Categories: frequentist/Bayesian, J. Berger, P-values, Statistics | 32 Comments

Egon Pearson’s Heresy

E.S. Pearson: 11 Aug 1895-12 June 1980.

Today is Egon Pearson’s birthday: 11 August 1895-12 June, 1980.
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, some people concentrate to an absurd extent on “science-wise error rates in dichotomous screening”.)

When Erich Lehmann, in his review of my “Error and the Growth of Experimental Knowledge” (EGEK 1996), called Pearson “the hero of Mayo’s story,” it was because I found in E.S.P.’s work, if only in brief discussions, hints, and examples, the key elements for an “inferential” or “evidential” interpretation of N-P statistics. Granted, these “evidential” attitudes and practices have never been explicitly codified to guide the interpretation of N-P tests. If they had been, I would not be on about providing an inferential philosophy all these years.[i] Nevertheless, “Pearson and Pearson” statistics (both Egon, not Karl) would have looked very different from Neyman and Pearson statistics, I suspect. One of the few sources of E.S. Pearson’s statistical philosophy is his (1955) “Statistical Concepts in Their Relation to Reality”. It begins like this: Continue reading

Categories: phil/history of stat, Philosophy of Statistics, Statistics | Tags: , | 2 Comments

Blog Contents: June and July 2014

Image of business woman rolling a giant stone

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Blog Contents: June and July 2014*

(6/5) Stephen Senn: Blood Simple? The complicated and controversial world of bioequivalence (guest post)

(6/9) “The medical press must become irrelevant to publication of clinical trials.”

(6/11) A. Spanos: “Recurring controversies about P values and confidence intervals revisited”

(6/14) “Statistical Science and Philosophy of Science: where should they meet?”

(6/21) Big Bayes Stories? (draft ii)

(6/25) Blog Contents: May 2014

(6/28) Sir David Hendry Gets Lifetime Achievement Award

(6/30) Some ironies in the ‘replication crisis’ in social psychology (4th and final installment) Continue reading

Categories: blog contents | Leave a comment

Winner of July Palindrome: Manan Shah

Shah

Manan Shah

Winner of July 2014 Contest:

Manan Shah

Palindrome: 

Trap May Elba, Dr. of Fanatic. I fed naan, deli-oiled naan, deficit an affordable yam part.

The requirements: 

In addition to using Elba, a candidate for a winning palindrome must have used fanatic. An optional second word was: part. An acceptable palindrome with both words would best an acceptable palindrome with just fanatic

Bio:

Manan Shah is a mathematician and owner of Think. Plan. Do. LLC. (www.ThinkPlanDoLLC.com). He also maintains the “Math Misery?” blog at www.mathmisery.com. He holds a PhD in Mathematics from Florida State University.

Continue reading

Categories: Palindrome, Rejected Posts | Leave a comment

What did Nate Silver just say? Blogging the JSM 2013

imagesMemory Lane: August 6, 2013. My initial post on JSM13 (8/5/13) was here.

Nate Silver gave his ASA Presidential talk to a packed audience (with questions tweeted[i]). Here are some quick thoughts—based on scribbled notes (from last night). Silver gave a list of 10 points that went something like this (turns out there were 11):

1. statistics are not just numbers

2. context is needed to interpret data

3. correlation is not causation

4. averages are the most useful tool

5. human intuitions about numbers tend to be flawed and biased

6. people misunderstand probability

7. we should be explicit about our biases and (in this sense) should be Bayesian?

8. complexity is not the same as not understanding

9. being in the in crowd gets in the way of objectivity

10. making predictions improves accountability Continue reading

Categories: Statistics, StatSci meets PhilSci | 3 Comments

Neyman, Power, and Severity

April 16, 1894 – August 5, 1981

NEYMAN: April 16, 1894 – August 5, 1981

Jerzy Neyman: April 16, 1894-August 5, 1981. This reblogs posts under “The Will to Understand Power” & “Neyman’s Nursery” here & here.

Way back when, although I’d never met him, I sent my doctoral dissertation, Philosophy of Statistics, to one person only: Professor Ronald Giere. (And he would read it, too!) I knew from his publications that he was a leading defender of frequentist statistical methods in philosophy of science, and that he’d worked for at time with Birnbaum in NYC.

Some ten 15 years ago, Giere decided to quit philosophy of statistics (while remaining in philosophy of science): I think it had to do with a certain form of statistical exile (in philosophy). He asked me if I wanted his papers—a mass of work on statistics and statistical foundations gathered over many years. Could I make a home for them? I said yes. Then came his caveat: there would be a lot of them.

As it happened, we were building a new house at the time, Thebes, and I designed a special room on the top floor that could house a dozen or so file cabinets. (I painted it pale rose, with white lacquered book shelves up to the ceiling.) Then, for more than 9 months (same as my son!), I waited . . . Several boxes finally arrived, containing hundreds of files—each meticulously labeled with titles and dates.  More than that, the labels were hand-typed!  I thought, If Ron knew what a slob I was, he likely would not have entrusted me with these treasures. (Perhaps he knew of no one else who would  actually want them!) Continue reading

Categories: Neyman, phil/history of stat, power, Statistics | Tags: , , , | 4 Comments

Blogging Boston JSM2014?

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I’m not there. (Several people have asked, I guess because I blogged JSM13.) If you hear of talks (or anecdotes) of interest to error statistics.com, please comment here (or twitter: @learnfromerror)

Categories: Announcement | 7 Comments

Roger Berger on Stephen Senn’s “Blood Simple” with a response by Senn (Guest posts)

Roger BergerRoger L. Berger

School Director & Professor
School of Mathematical & Natural Science
Arizona State University

Comment on S. Senn’s post: Blood Simple? The complicated and controversial world of bioequivalence”(*)

First, I do agree with Senn’s statement that “the FDA requires conventional placebo-controlled trials of a new treatment to be tested at the 5% level two-sided but since they would never accept a treatment that was worse than placebo the regulator’s risk is 2.5% not 5%.” The FDA procedure essentially defines a one-sided test with Type I error probability (size) of .025. Why it is not just called this, I do not know. And if the regulators believe .025 is the appropriate Type I error probability, then perhaps it should be used in other situations, e.g., bioequivalence testing, as well.

Senn refers to a paper by Hsu and me (Berger and Hsu (1996)), and then attempts to characterize what we said. Unfortunately, I believe he has mischaracterized. Continue reading

Categories: bioequivalence, frequentist/Bayesian, PhilPharma, Statistics | Tags: , | 22 Comments

S. Senn: “Responder despondency: myths of personalized medicine” (Guest Post)

Stephen Senn

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Stephen Senn
Head, Methodology and Statistics Group
Competence Center for Methodology and Statistics (CCMS)
Luxembourg

Responder despondency: myths of personalized medicine

The road to drug development destruction is paved with good intentions. The 2013 FDA report, Paving the Way for Personalized Medicine  has an encouraging and enthusiastic foreword from Commissioner Hamburg and plenty of extremely interesting examples stretching back decades. Given what the report shows can be achieved on occasion, given the enthusiasm of the FDA and its commissioner, given the amazing progress in genetics emerging from the labs, a golden future of personalized medicine surely awaits us. It would be churlish to spoil the party by sounding a note of caution but I have never shirked being churlish and that is exactly what I am going to do. Continue reading

Categories: evidence-based policy, Statistics, Stephen Senn | 49 Comments

Continued:”P-values overstate the evidence against the null”: legit or fallacious?

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continued…

Categories: Bayesian/frequentist, CIs and tests, fallacy of rejection, highly probable vs highly probed, P-values, Statistics | 39 Comments

“P-values overstate the evidence against the null”: legit or fallacious? (revised)

0. July 20, 2014: Some of the comments to this post reveal that using the word “fallacy” in my original title might have encouraged running together the current issue with the fallacy of transposing the conditional. Please see a newly added Section 7.

Continue reading

Categories: Bayesian/frequentist, CIs and tests, fallacy of rejection, highly probable vs highly probed, P-values, Statistics | 71 Comments

Higgs discovery two years on (2: Higgs analysis and statistical flukes)

Higgs_cake-sI’m reblogging a few of the Higgs posts, with some updated remarks, on this two-year anniversary of the discovery. (The first was in my last post.) The following, was originally “Higgs Analysis and Statistical Flukes: part 2″ (from March, 2013).[1]

Some people say to me: “This kind of reasoning is fine for a ‘sexy science’ like 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.  Continue reading

Categories: Higgs, highly probable vs highly probed, P-values, Severity, Statistics | 13 Comments

Higgs Discovery two years on (1: “Is particle physics bad science?”)

Higgs_cake-s

July 4, 2014 was the two year anniversary of the Higgs boson discovery. As the world was celebrating the “5 sigma!” announcement, and we were reading about the statistical aspects of this major accomplishment, I was aghast to be emailed a letter, purportedly instigated by Bayesian Dennis Lindley, through Tony O’Hagan (to the ISBA). Lindley, according to this letter, wanted to know:

“Are the particle physics community completely wedded to frequentist analysis?  If so, has anyone tried to explain what bad science that is?”

Fairly sure it was a joke, I posted it on my “Rejected Posts” blog for a bit until it checked out [1]. (See O’Hagan’s “Digest and Discussion”) Continue reading

Categories: Bayesian/frequentist, fallacy of non-significance, Higgs, Lindley, Statistics | Tags: , , , , , | 4 Comments

Winner of June Palindrome Contest: Lori Wike

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Winner of June 2014 Palindrome Contest: First Second* Time Winner! Lori Wike

*Her April win is here

Palindrome:

Parsec? I overfit omen as Elba sung “I err on! Oh, honor reign!” Usable, sane motif revoices rap.

The requirement: A palindrome with Elba plus overfit. (The optional second word: “average” was not needed to win.)

Bio:

Lori Wike is principal bassoonist of the Utah Symphony and is on the faculty of the University of Utah and Westminster College. She holds a Bachelor of Music degree from the Eastman School of Music and a Master of Arts degree in Comparative Literature from UC-Irvine.

Continue reading

Categories: Announcement, Palindrome | Leave a comment

Some ironies in the ‘replication crisis’ in social psychology (4th and final installment)

freud mirror espThere are some ironic twists in the way social psychology is dealing with its “replication crisis”, and they may well threaten even the most sincere efforts to put the field on firmer scientific footing–precisely in those areas that evoked the call for a “daisy chain” of replications. Two articles, one from the Guardian (June 14), and a second from The Chronicle of Higher Education (June 23) lay out the sources of what some are calling “Repligate”. The Guardian article is “Physics Envy: Do ‘hard’ sciences hold the solution to the replication crisis in psychology?”

The article in the Chronicle of Higher Education also gets credit for its title: “Replication Crisis in Psychology Research Turns Ugly and Odd”. I’ll likely write this in installments…(2nd, 3rd , 4th)

^^^^^^^^^^^^^^^

The Guardian article answers yes to the question “Do ‘hard’ sciences hold the solution“:

Psychology is evolving faster than ever. For decades now, many areas in psychology have relied on what academics call “questionable research practices” – a comfortable euphemism for types of malpractice that distort science but which fall short of the blackest of frauds, fabricating data.
Continue reading

Categories: junk science, science communication, Statistical fraudbusting, Statistics | 53 Comments

Sir David Hendry Gets Lifetime Achievement Award

images-17Sir David Hendry, Professor of Economics at the University of Oxford [1], was given the Celebrating Impact Lifetime Achievement Award on June 8, 2014. Professor Hendry presented his automatic model selection program (Autometrics) at our conference, Statistical Science and Philosophy of Science (June, 2010) (Site is here.) I’m posting an interesting video and related links. I invite comments on the paper Hendry published, “Empirical Economic Model Discovery and Theory Evaluation,” in our special volume of Rationality, Markets, and Morals (abstract below). [2]

One of the world’s leading economists, INET Oxford’s Prof. Sir David Hendry received a unique award from the Economic and Social Research Council (ESRC)…
Continue reading

Categories: David Hendry, StatSci meets PhilSci | Tags: | Leave a comment

Blog Contents: May 2014

metablog old fashion typewriter

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May 2014

(5/1) Putting the brakes on the breakthrough: An informal look at the argument for the Likelihood Principle

(5/3) You can only become coherent by ‘converting’ non-Bayesianly

(5/6) Winner of April Palindrome contest: Lori Wike

(5/7) A. Spanos: Talking back to the critics using error statistics (Phil6334)

(5/10) Who ya gonna call for statistical Fraudbusting? R.A. Fisher, P-values, and error statistics (again)

(5/15) Scientism and Statisticism: a conference* (i) Continue reading

Categories: blog contents, Metablog, Statistics | Leave a comment

Big Bayes Stories? (draft ii)

images-15“Wonderful examples, but let’s not close our eyes,”  is David J. Hand’s apt title for his discussion of the recent special issue (Feb 2014) of Statistical Science called Big Bayes Stories” (edited by Sharon McGrayne, Kerrie Mengersen and Christian Robert.) For your Saturday night/ weekend reading, here are excerpts from Hand, another discussant (Welsh), scattered remarks of mine, along with links to papers and background. I begin with David Hand:

 [The papers in this collection] give examples of problems which are well-suited to being tackled using such methods, but one must not lose sight of the merits of having multiple different strategies and tools in one’s inferential armory.(Hand [1])_

…. But I have to ask, is the emphasis on ‘Bayesian’ necessary? That is, do we need further demonstrations aimed at promoting the merits of Bayesian methods? … The examples in this special issue were selected, firstly by the authors, who decided what to write about, and then, secondly, by the editors, in deciding the extent to which the articles conformed to their desiderata of being Bayesian success stories: that they ‘present actual data processing stories where a non-Bayesian solution would have failed or produced sub-optimal results.’ In a way I think this is unfortunate. I am certainly convinced of the power of Bayesian inference for tackling many problems, but the generality and power of the method is not really demonstrated by a collection specifically selected on the grounds that this approach works and others fail. To take just one example, choosing problems which would be difficult to attack using the Neyman-Pearson hypothesis testing strategy would not be a convincing demonstration of a weakness of that approach if those problems lay outside the class that that approach was designed to attack.

Hand goes on to make a philosophical assumption that might well be questioned by Bayesians: Continue reading

Categories: Bayesian/frequentist, Honorary Mention, Statistics | 62 Comments

“Statistical Science and Philosophy of Science: where should they meet?”

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Four score years ago (!) we held the conference “Statistical Science and Philosophy of Science: Where Do (Should) They meet?” at the London School of Economics, Center for the Philosophy of Natural and Social Science, CPNSS, where I’m visiting professor [1] Many of the discussions on this blog grew out of contributions from the conference, and conversations initiated soon after. The conference site is here; my paper on the general question is here.[2]

My main contribution was “Statistical Science Meets Philosophy of Science Part 2: Shallow versus Deep Explorations” SS & POS 2. It begins like this: 

1. Comedy Hour at the Bayesian Retreat[3]

 Overheard at the comedy hour at the Bayesian retreat: Did you hear the one about the frequentist… Continue reading

Categories: Error Statistics, Philosophy of Statistics, Severity, Statistics, StatSci meets PhilSci | 23 Comments

A. Spanos: “Recurring controversies about P values and confidence intervals revisited”

A SPANOS

Aris Spanos
Wilson E. Schmidt Professor of Economics
Department of Economics, Virginia Tech

Recurring controversies about P values and confidence intervals revisited*
Ecological Society of America (ESA) ECOLOGY
Forum—P Values and Model Selection (pp. 609-654)
Volume 95, Issue 3 (March 2014): pp. 645-651

INTRODUCTION

The use, abuse, interpretations and reinterpretations of the notion of a P value has been a hot topic of controversy since the 1950s in statistics and several applied fields, including psychology, sociology, ecology, medicine, and economics.

The initial controversy between Fisher’s significance testing and the Neyman and Pearson (N-P; 1933) hypothesis testing concerned the extent to which the pre-data Type  I  error  probability  α can  address the arbitrariness and potential abuse of Fisher’s post-data  threshold for the value. Continue reading

Categories: CIs and tests, Error Statistics, Fisher, P-values, power, Statistics | 32 Comments

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