Statistics

Er, about those “other statistical approaches”: Hold off until a balanced critique is in?

Posted on April 1, 2016 by Mayo

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)

“Reaching for Best Practices in Statistics: Proceed with Caution Until a Balanced Critique Is In”

J. Hossiason

“[A]ll of the other approaches*, as well as most statistical tools, may suffer from many of the same problems as the p-values do. What level of likelihood ratio in favor of the research hypothesis will be acceptable to the journal? Should scientific discoveries be based on whether posterior odds pass a specific threshold (P3)? Does either measure the size of an effect (P5)?…How can we decide about the sample size needed for a clinical trial—however analyzed—if we do not set a specific bright-line decision rule? 95% confidence intervals or credence intervals…offer no protection against selection when only those that do not cover 0, are selected into the abstract (P4). (Benjamini, ASA commentary, pp. 3-4)

What’s sauce for the goose is sauce for the gander right? Many statisticians seconded George Cobb who urged “the board to set aside time at least once every year to consider the potential value of similar statements” to the recent ASA p-value report. Disappointingly, a preliminary survey of leaders in statistics, many from the original p-value group, aired striking disagreements on best and worst practices with respect to these other approaches. The Executive Board is contemplating a variety of recommendations, minimally, Continue reading →

Categories: Bayesian/frequentist, Statistics | 84 Comments

A. Spanos: Talking back to the critics using error statistics

Posted on March 26, 2016 by Mayo

Given all the recent attention given to kvetching about significance tests, it’s an apt time to reblog Aris Spanos’ overview of the error statistician talking back to the critics [1]. A related paper for your Saturday night reading is Mayo and Spanos (2011).[2] It mixes the error statistical philosophy of science with its philosophy of statistics, introduces severity, and responds to 13 criticisms and howlers.

I’m going to comment on some of the ASA discussion contributions I hadn’t discussed earlier. Please share your thoughts in relation to any of this.

[1]It was first blogged here, as part of our seminar 2 years ago.

[2] For those seeking a bit more balance to the main menu offered in the ASA Statistical Significance Reference list.

All She Wrote (so far): Error Statistics Philosophy: 4.5 years on

Posted on March 22, 2016 by Mayo

D.G. Mayo with her blogging typewriter

Error Statistics Philosophy: Blog Contents (4.5 years)
By: D. G. Mayo [i]

Dear Reader: It’s hard to believe I’ve been blogging for 4 and a half years (since Sept. 3, 2011)! A big celebration is taking place at the Elbar Room as I type this. Please peruse the offerings below, and take advantage of some of the super contributions and discussions by guest posters and readers! I don’t know how much longer I’ll continue blogging, but at least until the publication of my my book, “How to Tell What’s True About Statistical Inference.” After that I plan to run conferences, workshops, and ashrams on PhilStat and PhilSci, and will invite readers to take part! Keep reading and commenting. Sincerely, D. Mayo

September 2011

(9/3) Frequentists in Exile: The Purpose of this Blog
(9/3) Overheard at the comedy hour at the Bayesian retreat
(9/4) Drilling Rule #1
(9/9) Kuru
(9/13) In Exile, Clinging to Old Ideas?
.
(9/15) SF conferences & E. Lehmann
(9/16) Getting It Right But for the Wrong Reason
(9/20) A Highly Anomalous Event
(9/23) LUCKY 13 (Critcisms)
(9/26) Whipping Boys and Witch Hunters
(9/29) Part 1: Imaginary scientist at an imaginary company, Prionvac, and an imaginary reformer

Continue reading →

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

Your chance to continue the “due to chance” discussion in roomier quarters

Posted on March 19, 2016 by Mayo

Comments get unwieldy after 100, so here’s a chance to continue the “due to chance” discussion in some roomier quarters. (There seems to be at least two distinct lanes being travelled.) Now one of the main reasons I run this blog is to discover potential clues to solving or making progress on thorny philosophical problems I’ve been wrangling with for a long time. I think I extracted some illuminating gems from the discussion here, but I don’t have time to write them up, and won’t for a bit, so I’ve parked a list of comments wherein the golden extracts lie (I think) over at my Rejected Posts blog[1]. (They’re all my comments, but as influenced by readers, so I thank you!) Over there, there’s no “return and resubmit”, but around a dozen posts have eventually made it over here, tidied up. Please continue the discussion on this blog (I don’t even recommend going over there). You can link to your earlier comments by clicking on the date.

[1] The Spiegelhalter (PVP) link is here.

Categories: Error Statistics, P-values, Rejected Posts, Statistics | 36 Comments

“A small p-value indicates it’s improbable that the results are due to chance alone” –fallacious or not? (more on the ASA p-value doc)

Posted on March 12, 2016 by Mayo

There’s something about “Principle 2” in the ASA document on p-values that I couldn’t address in my brief commentary, but is worth examining more closely.

2. P-values do not measure (a) the probability that the studied hypothesis is true , or (b) the probability that the data were produced by random chance alone,

(a) is true, but what about (b)? That’s what I’m going to focus on, because I think it is often misunderstood. It was discussed earlier on this blog in relation to the Higgs experiments and deconstructing “the probability the results are ‘statistical flukes'”. So let’s examine: Continue reading →

Categories: P-values, statistical tests, Statistics | 170 Comments

Don’t throw out the error control baby with the bad statistics bathwater

Posted on March 7, 2016 by Mayo

My invited comments on the ASA Document on P-values*

The American Statistical Association is to be credited with opening up a discussion into p-values; now an examination of the foundations of other key statistical concepts is needed.

Statistical significance tests are a small part of a rich set of “techniques for systematically appraising and bounding the probabilities (under respective hypotheses) of seriously misleading interpretations of data” (Birnbaum 1970, p. 1033). These may be called error statistical methods (or sampling theory). The error statistical methodology supplies what Birnbaum called the “one rock in a shifting scene” (ibid.) in statistical thinking and practice. Misinterpretations and abuses of tests, warned against by the very founders of the tools, shouldn’t be the basis for supplanting them with methods unable or less able to assess, control, and alert us to erroneous interpretations of data. Continue reading →

Categories: Error Statistics, P-values, science communication, Statistics | 21 Comments

Repligate Returns (or, the non-significance of non-significant results, are the new significant results)

Posted on March 4, 2016 by Mayo

unscrambling soap words clears me of this deed (aosp)

Remember “Repligate”? [“Some Ironies in the Replication Crisis in Social Psychology“] and, more recently, the much publicized attempt to replicate 100 published psychology articles by the Open Science Collaboration (OSC) [“The Paradox of Replication“]? Well, some of the critics involved in Repligate have just come out with a criticism of the OSC results, claiming they’re way, way off in their low estimate of replications in psychology [1]. (The original OSC report is here.) I’ve only scanned the critical article quickly, but some bizarre statistical claims leap out at once. (Where do they get this notion about confidence intervals?) It’s published in Science! There’s also a response from the OSC researchers. Neither group adequately scrutinizes the validity of many of the artificial experiments and proxy variables–an issue I’ve been on about for a while. Without firming up the statistics-research link, no statistical fixes can help. I’m linking to the articles here for your weekend reading. I invite your comments! For some reason a whole bunch of items of interest, under the banner of “statistics and the replication crisis,” are all coming out at around the same time, and who can keep up? March 7 brings yet more! (Stay tuned). Continue reading →

Categories: replication research, reproducibility, Statistics | 21 Comments

Statistical Challenges in Assessing and Fostering the Reproducibility of Scientific Results

Posted on February 29, 2016 by Mayo

Statistical Challenges in Assessing and Fostering the Reproducibility of Scientific Results

I generally find National Academy of Science (NAS) manifestos highly informative. I only gave a quick reading to around 3/4 of this one. I thank Hilda Bastian for twittering the link. Before giving my impressions, I’m interested to hear what readers think, whenever you get around to having a look. Here’s from the intro*:

Questions about the reproducibility of scientific research have been raised in numerous settings and have gained visibility through several high-profile journal and popular press articles. Quantitative issues contributing to reproducibility challenges have been considered (including improper data management and analysis, inadequate statistical expertise, and incomplete data, among others), but there is no clear consensus on how best to approach or to minimize these problems…
Continue reading →

Categories: Error Statistics, replication research, reproducibility, Statistics | 53 Comments

3 YEARS AGO (FEBRUARY 2013): MEMORY LANE

Posted on February 27, 2016 by Mayo

3 years ago…

MONTHLY MEMORY LANE: 3 years ago: February 2013. I mark in red three posts that seem most apt for general background on key issues in this blog [1]. Posts that are part of a “unit” or a group of “U-Phils”(you [readers] philosophize) count as one. Feb. 2013 reminds me how much the issue of the Likelihood Principle figured in this blog. I group the 4 on the Likelihood Principle, in burgundy, as one. Those unaware of the issue, or updating a statistics text in the next few months, might want to see what all the hoopla is about. (For the latest, please see [2]). The three in green are on Fisher. New questions or comments on any posts can be placed on this post.

February 2013

(2/2) U-Phil: Ton o’ Bricks
(2/4) January Palindrome Winner
(2/6) Mark Chang (now) gets it right about circularity
(2/8) From Gelman’s blog: philosophy and the practice of Bayesian statistics
(2/9) New kvetch: Filly Fury
(2/10) U-PHIL: Gandenberger & Hennig: Blogging Birnbaum’s Proof
(2/11) U-Phil: Mayo’s response to Hennig and Gandenberger
(2/13) Statistics as a Counter to Heavyweights…who wrote this?
(2/16) Fisher and Neyman after anger management?
(2/17) R. A. Fisher: how an outsider revolutionized statistics
(2/20) Fisher: from ‘Two New Properties of Mathematical Likelihood’
(2/23) Stephen Senn: Also Smith and Jones
(2/26) PhilStock: DO < $70
(2/26) Statistically speaking…

[1] I exclude those reblogged fairly recently. Monthly memory lanes began at the blog’s 3-year anniversary in Sept, 2014.

[2] The discussion culminated in this publication in Statistical Science. For a very informal, final, look, see this post.

Categories: 3-year memory lane, Statistics | Leave a comment

Deconstructing the Fisher-Neyman conflict wearing fiducial glasses (continued)

Posted on February 20, 2016 by Mayo

Fisher/ Neyman

This continues my previous post: “Can’t take the fiducial out of Fisher…” in recognition of Fisher’s birthday, February 17. I supply a few more intriguing articles you may find enlightening to read and/or reread on a Saturday night

Move up 20 years to the famous 1955/56 exchange between Fisher and Neyman. Fisher clearly connects Neyman’s adoption of a behavioristic-performance formulation to his denying the soundness of fiducial inference. When “Neyman denies the existence of inductive reasoning, he is merely expressing a verbal preference. For him ‘reasoning’ means what ‘deductive reasoning’ means to others.” (Fisher 1955, p. 74).

Fisher was right that Neyman’s calling the outputs of statistical inferences “actions” merely expressed Neyman’s preferred way of talking. Nothing earth-shaking turns on the choice to dub every inference “an act of making an inference”.[i] The “rationality” or “merit” goes into the rule. Neyman, much like Popper, had a good reason for drawing a bright red line between his use of probability (for corroboration or probativeness) and its use by ‘probabilists’ (who assign probability to hypotheses). Fisher’s Fiducial probability was in danger of blurring this very distinction. Popper said, and Neyman would have agreed, that he had no problem with our using the word induction so long it was kept clear it meant testing hypotheses severely. Continue reading →

Categories: fiducial probability, Fisher, Neyman, Statistics | 57 Comments

Can’t Take the Fiducial Out of Fisher (if you want to understand the N-P performance philosophy) [i]

Posted on February 17, 2016 by Mayo

R.A. Fisher: February 17, 1890 – July 29, 1962

In recognition of R.A. Fisher’s birthday today, I’ve decided to share some thoughts on a topic that has so far has been absent from this blog: Fisher’s fiducial probability. Happy Birthday Fisher.

[Neyman and Pearson] “began an influential collaboration initially designed primarily, it would seem to clarify Fisher’s writing. This led to their theory of testing hypotheses and to Neyman’s development of confidence intervals, aiming to clarify Fisher’s idea of fiducial intervals (D.R.Cox, 2006, p. 195).

The entire episode of fiducial probability is fraught with minefields. Many say it was Fisher’s biggest blunder; others suggest it still hasn’t been understood. The majority of discussions omit the side trip to the Fiducial Forest altogether, finding the surrounding brambles too thorny to penetrate. Besides, a fascinating narrative about the Fisher-Neyman-Pearson divide has managed to bloom and grow while steering clear of fiducial probability–never mind that it remained a centerpiece of Fisher’s statistical philosophy. I now think that this is a mistake. It was thought, following Lehman (1993) and others, that we could take the fiducial out of Fisher and still understand the core of the Neyman-Pearson vs Fisher (or Neyman vs Fisher) disagreements. We can’t. Quite aside from the intrinsic interest in correcting the “he said/he said” of these statisticians, the issue is intimately bound up with the current (flawed) consensus view of frequentist error statistics.

So what’s fiducial inference? I follow Cox (2006), adapting for the case of the lower limit: Continue reading →

Categories: Error Statistics, fiducial probability, Fisher, Statistics | 18 Comments

Rubbing off, uncertainty, confidence, and Nate Silver

Posted on February 12, 2016 by Mayo

Polling estimation & rubbing off

Nate Silver describes “How we’re forecasting the primaries” using confidence intervals. Never mind that the estimates are a few weeks old, and put entirely to one side any predictions he makes or will make. I’m only interested in this one interpretive portion of the method, as Silver describes it:

In our interactive, you’ll see a bunch of funky-looking curves like the ones below for each candidate; they represent the model’s estimate of the possible distribution of his vote share. The red part of the curve represents a candidate’s 80 percent confidence interval. If the model is calibrated correctly, then he should finish within this range 80 percent of the time, above it 10 percent of the time, and below it 10 percent of the time. (My emphasis.)

OK. But when we look up the link to confidence interval, this seems to fall squarely within (what is correctly described as) the incorrect way to interpret intervals. Continue reading →

Categories: confidence intervals and tests, Statistics | 33 Comments

Philosophy-laden meta-statistics: Is “technical activism” free of statistical philosophy? (ii)

Posted on February 3, 2016 by Mayo

Ben Goldacre (of Bad Science), in a Nature article today (“Make Journals Report Clinical Trials Properly“), expresses puzzlement as to why bad statistical practices– “selective publication, inadequate descriptions of study methods that block efforts at replication, and data dredging through undisclosed use of multiple analytical strategies“–are continuing to occur even in the face of the new “technical activism” (a great term he introduces). Worse, these questionable practices are actually being defended by some medical journals. “[J]ournal editors now need to engage in a serious public discussion on why this is still happening“. Goldacre doesn’t consider that at least some of the pushback he’s seeing has a basis in statistical philosophy! I explain at the end. Here’s Goldacre (Feb 2, 2016; emphasis mine):

Science is in flux. The basics of a rigorous scientific method were worked out many years ago, but there is now growing concern about systematic structural flaws that undermine the integrity of published data: selective publication, inadequate descriptions of study methods that block efforts at replication, and data dredging through undisclosed use of multiple analytical strategies. Problems such as these undermine the integrity of published data and increase the risk of exaggerated or even false-positive findings, leading collectively to the ‘replication crisis’. Continue reading →

Categories: P-values, reforming the reformers, Statistics | 60 Comments

3 YEARS AGO (JANUARY 2013): MEMORY LANE

Posted on January 29, 2016 by Mayo

3 years ago…

MONTHLY MEMORY LANE: 3 years ago: January 2013. I mark in red three posts that seem most apt for general background on key issues in this blog [1]. Posts that are part of a “unit” or a group of “U-Phils”(you [readers] philosophize) count as one. It was tough to pick just 3 this month. I’m putting the 2 “U-Phils” in burgundy–nearly red. They involve reader contributions on the likelihood principle–a major topic in foundations of statistics. Please check out the others. New questions or comments can be placed on this post.

January 2013

(1/2) Severity as a ‘Metastatistical’ Assessment
(1/4) Severity Calculator
(1/6) Guest post: Bad Pharma? (S. Senn)
(1/9) RCTs, skeptics, and evidence-based policy
(1/10) James M. Buchanan
(1/11) Aris Spanos: James M. Buchanan: a scholar, teacher and friend
(1/12) Error Statistics Blog: Table of Contents
(1/15) Ontology & Methodology: Second call for Abstracts, Papers
(1/18) New Kvetch/PhilStock
(1/19) Saturday Night Brainstorming and Task Forces: (2013) TFSI on NHST (2015 update).
(1/22) New PhilStock
(1/23) P-values as posterior odds?
(1/26) Coming up: December U-Phil Contributions….
(1/27) U-Phil: S. Fletcher & N.Jinn
(1/30) U-Phil: J. A. Miller: Blogging the SLP

[1] I exclude those reblogged fairly recently. Monthly memory lanes began at the blog’s 3-year anniversary in Sept, 2014.

Categories: 3-year memory lane, Statistics | Leave a comment

High error rates in discussions of error rates (1/21/16 update)

Posted on January 19, 2016 by Mayo

27D0BB5300000578-3168627-image-a-27_1437433320306

waiting for the other shoe to drop…

Do you ever find yourself holding your breath when reading an exposition of significance tests that’s going swimmingly so far? If you’re a frequentist in exile, you know what I mean. I’m sure others feel this way too. When I came across Jim Frost’s posts on The Minitab Blog, I thought I might actually have located a success story. He does a good job explaining P-values (with charts), the duality between P-values and confidence levels, and even rebuts the latest “test ban” (the “Don’t Ask, Don’t Tell” policy). Mere descriptive reports of observed differences that the editors recommend, Frost shows, are uninterpretable without a corresponding P-value or the equivalent. So far, so good. I have only small quibbles, such as the use of “likelihood” when meaning probability, and various and sundry nitpicky things. But watch how in some places significance levels are defined as the usual error probabilities and error rates—indeed in the glossary for the site—while in others it is denied they provide error rates. In those other places, error probabilities and error rates shift their meaning to posterior probabilities, based on priors representing the “prevalence” of true null hypotheses. Continue reading →

Categories: highly probable vs highly probed, J. Berger, reforming the reformers, Statistics | 11 Comments

“On the Brittleness of Bayesian Inference,” Owhadi and Scovel (PUBLISHED)

Posted on January 11, 2016 by Mayo

The record number of hits on this blog goes to “When Bayesian Inference shatters,” where Houman Owhadi presents a “Plain Jane” explanation of results now published in “On the Brittleness of Bayesian Inference”. A follow-up was 1 year ago. Here’s how their paper begins:

Houman Owhadi
Professor of Applied and Computational Mathematics and Control and Dynamical Systems, Computing + Mathematical Sciences,
California Institute of Technology, USA+

Clint Scovel
Senior Scientist,
Computing + Mathematical Sciences,
California Institute of Technology, USA

“On the Brittleness of Bayesian Inference”

ABSTRACT: With the advent of high-performance computing, Bayesian methods are becoming increasingly popular tools for the quantification of uncertainty throughout science and industry. Since these methods can impact the making of sometimes critical decisions in increasingly complicated contexts, the sensitivity of their posterior conclusions with respect to the underlying models and prior beliefs is a pressing question to which there currently exist positive and negative answers. We report new results suggesting that, although Bayesian methods are robust when the number of possible outcomes is finite or when only a finite number of marginals of the data-generating distribution are unknown, they could be generically brittle when applied to continuous systems (and their discretizations) with finite information on the data-generating distribution. If closeness is defined in terms of the total variation (TV) metric or the matching of a finite system of generalized moments, then (1) two practitioners who use arbitrarily close models and observe the same (possibly arbitrarily large amount of) data may reach opposite conclusions; and (2) any given prior and model can be slightly perturbed to achieve any desired posterior conclusion. The mechanism causing brittleness/robustness suggests that learning and robustness are antagonistic requirements, which raises the possibility of a missing stability condition when using Bayesian inference in a continuous world under finite information.

Categories: Bayesian/frequentist, Statistics | 16 Comments

Preregistration Challenge: My email exchange

Posted on January 8, 2016 by Mayo

David Mellor, from the Center for Open Science, emailed me asking if I’d announce his Preregistration Challenge on my blog, and I’m glad to do so. You win $1,000 if your properly preregistered paper is published. The recent replication effort in psychology showed, despite the common refrain – “it’s too easy to get low P-values” – that in preregistered replication attempts it’s actually very difficult to get small P-values. (I call this the “paradox of replication”[1].) Here’s our e-mail exchange from this morning:

Dear Deborah Mayod,

I’m reaching out to individuals who I think may be interested in our recently launched competition, the Preregistration Challenge (https://cos.io/prereg). Based on your blogging, I thought it could be of interest to you and to your readers.

In case you are unfamiliar with it, preregistration specifies in advance the precise study protocols and analytical decisions before data collection, in order to separate the hypothesis-generating exploratory work from the hypothesis testing confirmatory work.

Though required by law in clinical trials, it is virtually unknown within the basic sciences. We are trying to encourage this new behavior by offering 1,000 researchers $1000 prizes for publishing the results of their preregistered work.

Please let me know if this is something you would consider blogging about or sharing in other ways. I am happy to discuss further.

Best,

David
David Mellor, PhD

Project Manager, Preregistration Challenge, Center for Open Science

Deborah Mayo To David: 10:33 AM (1 hour ago)

David: Yes I’m familiar with it, and I hope that it encourages people to avoid data-dependent determinations that bias results. It shows the importance of statistical accounts that can pick up on such biasing selection effects. On the other hand, coupling prereg with some of the flexible inference accounts now in use won’t really help. Moreover, there may, in some fields, be a tendency to research a non-novel, fairly trivial result.

And if they’re going to preregister, why not go blind as well? Will they?

Best,

Mayo Continue reading →

Categories: Announcement, preregistration, Statistical fraudbusting, Statistics | 11 Comments

Midnight With Birnbaum (Happy New Year)

Posted on December 31, 2015 by Mayo

Just as in the past 4 years since I’ve been blogging, I revisit that spot in the road at 11p.m., just outside the Elbar Room, get into a strange-looking taxi, and head to “Midnight With Birnbaum”. (The pic on the left is the only blurry image I have of the club I’m taken to.) I wonder if the car will come for me this year, given that my Birnbaum article has been out since 2014… The (Strong) Likelihood Principle–whether or not it is named–remains at the heart of many of the criticisms of Neyman-Pearson (N-P) statistics (and cognate methods). Yet as Birnbaum insisted, the “confidence concept” is the “one rock in a shifting scene” of statistical foundations, insofar as there’s interest in controlling the frequency of erroneous interpretations of data. (See my rejoinder.) Birnbaum bemoaned the lack of an explicit evidential interpretation of N-P methods. Maybe in 2016? Anyway, it’s 6 hrs later here, so I’m about to leave for that spot in the road…

You know how in that (not-so) recent Woody Allen movie, “Midnight in Paris,” the main character (I forget who plays it, I saw it on a plane) is a writer finishing a novel, and he 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? He is impressed when his work earns their approval and he comes back each night in the same mysterious cab…Well, imagine an error statistical philosopher is picked up in a mysterious taxi at midnight (New Year’s Eve ~~2011~~ ~~2012~~, ~~2013~~, ~~2014~~, 2015) and is taken back fifty years and, lo and behold, finds herself in the company of Allan Birnbaum.[i] There are a couple of brief (12/31/14 & 15) updates at the end.

ERROR STATISTICIAN: It’s wonderful to meet you Professor Birnbaum; I’ve always been extremely impressed with the important impact your work has had on philosophical foundations of statistics. I happen to be writing on your famous argument about the likelihood principle (LP). (whispers: I can’t believe this!)

BIRNBAUM: Ultimately you know I rejected the LP as failing to control the error probabilities needed for my Confidence concept.

ERROR STATISTICIAN: Yes, but I actually don’t think your argument shows that the LP follows from such frequentist concepts as sufficiency S and the weak conditionality principle WLP.[ii] Sorry,…I know it’s famous…

BIRNBAUM: Well, I shall happily invite you to take any case that violates the LP and allow me to demonstrate that the frequentist is led to inconsistency, provided she also wishes to adhere to the WLP and sufficiency (although less than S is needed).

ERROR STATISTICIAN: Well I happen to be a frequentist (error statistical) philosopher; I have recently (2006) found a hole in your proof,..er…well I hope we can discuss it.

BIRNBAUM: Well, well, well: I’ll bet you a bottle of Elba Grease champagne that I can demonstrate it! Continue reading →

Categories: Birnbaum Brakes, Statistics, strong likelihood principle | Tags: Birnbaum, fallacious argument, Likelihood Principle, proofs | 2 Comments

3 YEARS AGO (DECEMBER 2012): MEMORY LANE

Posted on December 26, 2015 by Mayo

3 years ago…

MONTHLY MEMORY LANE: 3 years ago: December 2012. I am to mark in red three posts that seem most apt for general background on key issues in this blog [1]. However, posts that are part of a “unit” or group of posts count as one, so I’m not really cheating with the 5 in red. The items in the “green” group can’t be considered “general background” but are just the thing for readers interested in an ongoing episode in philosophy of statistics and law (PhilStat/Law/Stock). The two “purples” (12/8 and 12/31) are about the strong likelihood principle (SLP), one of my favorite topics. Whether I will go to meet Allan Birnbaum on New Year’s Eve (as I have for the past 4 years) is not yet decided.

December 2012

(12/2) Normal Deviate’s blog on false discovery rates
(12/2) Statistical Science meets Philosophy of Science
(12/3) Mayo Commentary on Gelman & Robert’s paper
(12/6) Announcement: U-Phil Extension: Blogging the Likelihood Principle
(12/7) Nov. Palindrome Winner: Kepler
(12/8) Don’t Birnbaumize that experiment my friend*–updated reblog
(12/11) Announcement: Prof. Stephen Senn to lead LSE grad seminar: 12-12-12
(12/11) Mayo on S. Senn: “How Can We Cultivate Senn’s-Ability?”
(12/13) “Bad statistics”: crime or free speech?
(12/14) PhilStat/Law (“Bad Statistics” Cont.)
(12/17) PhilStat/Law/Stock: multiplicity and duplicity
(12/19) PhilStat/Law/Stock: more on “bad statistics”: Schachtman
(12/21) Rejected Post: Clinical Trial Statistics Doomed by Mayan Apocalypse?
(12/22) Msc kvetch: unfair but lawful discrimination (vs the irresistibly attractive)
(12/24) 13 well-worn criticisms of significance tests (and how to avoid them)
(12/27) 3 msc kvetches on the blog bagel circuit
(12/30) An established probability theory for hair comparison?“–is not — and never was”
(12/31) Midnight with Birnbaum-reblog

[1] I exclude those reblogged fairly recently. Monthly memory lanes began at the blog’s 3-year anniversary in Sept, 2014.

Categories: 3-year memory lane, Statistics | Leave a comment

Gelman on ‘Gathering of philosophers and physicists unaware of modern reconciliation of Bayes and Popper’

Posted on December 17, 2015 by Mayo

I’m reblogging Gelman’s post today: “Gathering of philosophers and physicists unaware of modern reconciliation of Bayes and Popper”. I concur with Gelman’s arguments against all Bayesian “inductive support” philosophies, and welcome the Gelman and Shalizi (2013) ‘meeting of the minds’ between an error statistical philosophy and Bayesian falsification (which I regard as a kind of error statistical Bayesianism). Just how radical a challenge these developments pose to other stripes of Bayesianism has yet to be explored. My comment on them is here.

Screen Shot 2015-12-16 at 11.17.09 PM

“Gathering of philosophers and physicists unaware of modern reconciliation of Bayes and Popper” by Andrew Gelman

Hiro Minato points us to a news article by physicist Natalie Wolchover entitled “A Fight for the Soul of Science.”

I have no problem with most of the article, which is a report about controversies within physics regarding the purported untestability of physics models such as string theory (as for example discussed by my Columbia colleague Peter Woit). Wolchover writes:

Whether the fault lies with theorists for getting carried away, or with nature, for burying its best secrets, the conclusion is the same: Theory has detached itself from experiment. The objects of theoretical speculation are now too far away, too small, too energetic or too far in the past to reach or rule out with our earthly instruments. . . .

Over three mild winter days, scholars grappled with the meaning of theory, confirmation and truth; how science works; and whether, in this day and age, philosophy should guide research in physics or the other way around. . . .

To social and behavioral scientists, this is all an old old story. Concepts such as personality, political ideology, and social roles are undeniably important but only indirectly related to any measurements. In social science we’ve forever been in the unavoidable position of theorizing without sharp confirmation or falsification, and, indeed, unfalsifiable theories such as Freudian psychology and rational choice theory have been central to our understanding of much of the social world.

But then somewhere along the way the discussion goes astray: Continue reading →

Categories: Bayesian/frequentist, Error Statistics, Gelman, Shalizi, Statistics | 20 Comments

Statistics

The Statistics Wars & Their Casualties

Blog links (references)

Reviews of Statistical Inference as Severe Testing (SIST)

Interviews & Debates on PhilStat (2020)

Interviews on PhilStat (2019)

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