Invitation to discuss the ASA Task Force on Statistical Significance and Replication

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The latest salvo in the statistics wars comes in the form of the publication of The ASA Task Force on Statistical Significance and Replicability, appointed by past ASA president Karen Kafadar in November/December 2019. (In the ‘before times’!) Its members are:

Linda Young, (Co-Chair), Xuming He, (Co-Chair) Yoav Benjamini, Dick De Veaux, Bradley Efron, Scott Evans, Mark Glickman, Barry Graubard, Xiao-Li Meng, Vijay Nair, Nancy Reid, Stephen Stigler, Stephen Vardeman, Chris Wikle, Tommy Wright, Karen Kafadar, Ex-officio. (Kafadar 2020)

The full report of this Task Force is in the The Annals of Applied Statistics, and on my blogpost. It begins:

In 2019 the President of the American Statistical Association (ASA) established a task force to address concerns that a 2019 editorial in The American Statistician (an ASA journal) might be mistakenly interpreted as official ASA policy. (The 2019 editorial recommended eliminating the use of “p < 0.05” and “statistically significant” in statistical analysis.) This document is the statement of the task force… (Benjamini et al. 2021)

On Monday, August 2, the National Institute of Statistical Science (NISS) will hold a public discussion whose focus is this report, and several of its members will be there. (See the announcement at the end of this post).

Kafadar, and the members of this task force, deserve a lot of credit for defying the popular movement to “abandon statistical significance” by unanimously declaring: “that the use of P -values and significance testing, properly applied and interpreted, are important tools that should not be abandoned”…

• P -values are valid statistical measures that provide convenient conventions for communicating the uncertainty inherent in quantitative results. Indeed, P -values and significance tests are among the most studied and best understood statistical procedures in the statistics literature.

• They are important tools that have advanced science through their proper application. …

• P-values and significance tests, when properly applied and interpreted, increase the rigor of the conclusions drawn from data.
(Benjamini et al. 2021)

If you follow this blog, you know that I have often discussed the 2019 editorial in The American Statistician to which this Task Force report refers: Wasserstein, Schirm and Lazar (2019), hereafter WSL 2019 (see blog links below). But now I’m inviting you to share your views on any aspects of the overall episode (ASAgate?) for posting on this blog. (Send them by August 31, 2021, info in Note [1]  .) I’d like to put together a blogpost with multiple authors, and multiple perspectives soon after. For background see this post. (For even more background, see the links at the end of this post.)

I first assumed WSL 2019 was a continuation of the 2016 ASA Statement on P-values, especially given how it is written. According to WSL 2019, the 2016 ASA Statement had “stopped just short of recommending that declarations of ‘statistical significance’ be abandoned”, and they announce: “We take that step here….‘statistically significant’—don’t say it and don’t use it”. The use of p-value thresholds to distinguish data that do and do not indicate various discrepancies from a test hypotheses are also verboten.) [1] In fact, it rejects any number of classifications of data: “[T]he problem is not that of having only two labels. Results should not be trichotomized, or indeed categorized into any number of groups…” (WSL 2019, p. 2).

To many, including then ASA president Karen Kafadar (2019), the position in WSL 2019 challenges the overall use of hypothesis tests even though it does not ban P-values:

Many of you have written of instances in which authors and journal editors—and even some ASA members—have mistakenly assumed this editorial represented ASA policy. The mistake is understandable: The editorial was co-authored by an official of the ASA.

Even our own ASA members are asking each other, “What do we tell our collaborators when they ask us what they should do about statistical hypothesis tests and p-values?”

So the ASA Board created the new Task Force in November 2019 “with a charge to develop thoughtful principles and practices that the ASA can endorse and share with scientists and journal editors.” (AMSTATNEWS 1 February 2020).

Several of its members will be at Monday’s NISS meeting. A panel session I organized at the 2020 JSM, (P-values and ‘Statistical Significance’: Deconstructing the Arguments), grew out of this episode (my contribution in the proceedings is here).

The Task Force worked quickly, despite the pandemic, giving its recommendations to the ASA Board early–in time for the Joint Statistical Meetings (JSM) at the end of July 2020. But the ASA didn’t “endorse and share” the Task Force’s recommendations, and for months the document has been in limbo, turned down for publication in numerous venues, until recently finding a home in the Annals of Applied Statistics. So, it is finally out. What does it say aside from what I have quoted above? I’m guessing that because the statements were unanimous, they couldn’t go much beyond some rather unobjectionable claims. It’s quite short, and there’s also an editorial by Kafadar (editor-in-chief of the journal) in the issue.

I imagine a statistical significance tester raising these objections to the task force report:

  1. It does not tell us why, properly used, p-values increase rigor—namely by enabling error probability control.
  2. It implicitly seems to accept the view that using thresholds means viewing test outcomes as leading directly to decisions or actions (as in the behavioristic interpretation of Neyman-Pearson tests), rather than as part of an appraisal of evidence.

In fact any time you test a claim (or compute power) you are implicitly using a threshold. It needs to be specified, in advance, that not all outcomes will be allowed to be taken as evidence for a given claim.

3. It doesn’t tell us what’s meant by “properly used”.

While this might be assumed to be uncontroversial, nowadays, you will sometimes hear critics aver ‘of course the tests are fine if properly used’, but then in the next breath suggest that this requires p-values to agree with quantities measuring very different things (since ‘that’s what people want’). Even the meaning of “abandon” statistical significance has become highly equivocal (e.g., Mcshane et al. 2019).

4. Others?  (Use the comments, or put them in your guest blog contributions).

On the meta-level, of course, she would be concerned about the communication break down suggested by the very fact that the ASA board felt the need to appoint a Task Force to dispel the supposition that a position advanced by its Executive Director reflects the views of the ASA itself.[3] Still, in today’s climate of anti-statistical significance test fervor, the Task Force’s pressing on to find a home for their report when the ASA declined to make it public is impressive, if not heroic. We need more of that type of independence if scientific integrity is to be restored. The Task Force deserves further accolades for sparing us, for once, a rehearsal of the well-known howlers of abusives of tests that have long been lampooned.

Here’s the announcement of the NISS Program for August 2, 2021 (5pm ET)

NISS Affiliates liaisons and representatives representing academia, industry and government institutions traditionally meet over lunch at JSM to catch up with one another and hear from speakers on a topic of current interest. This year, (even though this event takes place in the evening) the ‘luncheon’ speakers featured will be Karen Kafadar, the 2019 ASA President from the University of Virginia.  Karen initiated the ASA Task Force on Statistical Significance and Replicability during her presidential year, that was convened to address issues surrounding the use of p-values and statistical significance, as well as their connection to replicability.  Xuming He from the University of Michigan and Linda Young from NASS who both served as co-chairs of this Task Force will summarize the discussion leading to the final report, and invite other task force members to join the discussion.

Luncheon Speakers: Karen Kafadar- 2019 ASA President, (University of Virginia), Xuming He – Task Force Co-chair, (University of Michigan), Linda Young – Task Force Co-chair, (NASS), Steven Stigler, (University of Chicago), Nancy Reid (University of Toronto) and Yoav Benjamini, (Tel Aviv University)..

This is a Special Event that is open to the public. You need not be a member of a NISS Affiliate Institution to attend the event this year.  Invite your colleagues!

NOTES

[1]They can be as long as you think apt. We can always post part of your commentary and link to the remainder (write me with questions). All who havve their guest post included will receive a free copy of my book: Statistical Inference as Severe Testing: How to Get Beyond the Statistics Wars (2018, CUP)

[2] Since the clarification was not made public until December 2019, an editorial I wrote “P-value thresholds, forfeit at your peril” mistakenly referred to WSL 2019 as ASA II https://errorstatistics.files.wordpress.com/2019/11/mayo-2019-forfeit-own-peril-european_journal_of_clinical_investigation-2.pdf

[3] It would seem that a public disclaimer by the authors sent around to members and journals would have avoided this. Kafadar had indicated early on that the Task Force recommendations would include a call for a “Disclaimer on all publications, articles, editorials, … authored by ASA Staff (e.g., as required for U.S. Govt employees)”.
At any rate, this was in Kafadar’s slide presentation at our JSM forum. Perhaps at Monday’s forum someone will ask: Why was that sensible recommendation deleted from the final report?

REFERENCES:

Benjamini, Y., De Veaux, R., Efron, B., et al. (2021). The ASA President’s task force statement on statistical significance and replicability. The Annals of Applied Statistics. (Online June 20, 2021.)

Kafadar, K. (2019). “The Year in Review … And More to Come”. AmStat News3 (Dec. 2019)

Kafadar, K. (2020). “Task Force on Statistical Significance and Replicability”. ASA Amstat Blog (Feb. 1, 2020).

Kafadar, K. (2021) “Editorial: Statistical Significance, P-Values, and Replicability“. The Annals of Applied Statistics

Mayo, D. G. (2020). Rejecting Statistical Significance Tests: Defanging the Arguments. In JSM Proceedings, Statistical Consulting Section. Alexandria, VA: American Statistical Association. 236-256.

Mayo, D. G. (2019). P-value Thresholds: Forfeit at Your Peril,European Journal of Clinical Investigation 49(10). EJCI-2019-0447

McShane, B. B., Gal, D., Gelman, A., Robert, C., & Tackett, J. L. (2019). Abandon statistical significance. American Statistician, 73, 235–245.

Wasserstein R. & Lazar, N. “The ASA’s Statement on p-Values: Context, Process, and Purpose,” The American Statistician 70(129 )(2016); see “The American Statistical Association’s Statement on and of Significance” (March 17, 2016).

Wasserstein, R., Schirm, A,. & Lazar, N. (2019). Moving to a world beyond “p < 0.05” (Editorial). The American Statistician 73(S1), 1–19.  https://doi.org/10.1080/00031305.2019.1583913

 

(SELECTED) BLOGPOSTS ON WSL 2019 FROM ERRORSTATISTICS.COM:

March 25, 2019: “Diary for Statistical War Correspondents on the Latest Ban on Speech.”

June 17, 2019: “The 2019 ASA Guide to P-values and Statistical Significance: Don’t Say What You Don’t Mean” (Some Recommendations)(ii)

July 19, 2019: “The NEJM Issues New Guidelines on Statistical Reporting: Is the ASA P-Value Project Backfiring? (i)”

September 19, 2019: “(Excerpts from) ‘P-Value Thresholds: Forfeit at Your Peril’ (free access).” The article by Hardwicke and Ioannidis (2019), and the editorials by Gelman and by me are linked on this post.

November 4, 2019: “On some Self-defeating aspects of the ASA’s 2019 recommendations of statistical significance tests”

November 14, 2019: “The ASA’s P-value Project: Why it’s Doing More Harm than Good (cont from 11/4/19)”

November 30, 2019: “P-Value Statements and Their Unintended(?) Consequences: The June 2019 ASA President’s Corner (b)”

December 13, 2019: “’Les stats, c’est moi’: We take that step here! (Adopt our fav word or phil stat!)(iii)”

         August 4, 2020: “August 6: JSM 2020 Panel on P-values & ‘Statistical significance’”

October 16, 2020: “The P-Values Debate”

December 13, 2020: “The Statistics Debate (NISS) in Transcript Form”

January 9, 2021: “Why hasn’t the ASA Board revealed the recommendations of its new task force on statistical significance and replicability?”

June 20, 2021:  “At Long Last! The ASA President’s Task Force Statement on Statistical Significance and Replicability”

June 28, 2021: “Statisticians Rise Up To Defend (error statistical) Hypothesis Testing”

Kafadar, K. (2020) JSM slides.

 

Categories: 2016 ASA Statement on P-values, ASA Task Force on Significance and Replicability, JSM 2020, National Institute of Statistical Sciences (NISS), statistical significance tests | 1 Comment

Statistics and the Higgs Discovery: 9 yr Memory Lane

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

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

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

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

Continue reading

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

Statisticians Rise Up To Defend (error statistical) Hypothesis Testing

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What is the message conveyed when the board of a professional association X appoints a Task Force intended to dispel the supposition that a position advanced by the Executive Director of association X does not reflect the views of association X on a topic that members of X disagree on? What it says to me is that there is a serious break-down of communication amongst the leadership and membership of that association. So while I’m extremely glad that the ASA appointed the Task Force on Statistical Significance and Replicability in 2019, I’m very sorry that the main reason it was needed was to address concerns that an editorial put forward by the ASA Executive Director (and 2 others) “might be mistakenly interpreted as official ASA policy”. The 2021 Statement of the Task Force (Benjamini et al. 2021) explains:

In 2019 the President of the American Statistical Association (ASA) established a task force to address concerns that a 2019 editorial in The American Statistician (an ASA journal) might be mistakenly interpreted as official ASA policy. (The 2019 editorial recommended eliminating the use of “p < 0.05” and “statistically significant” in statistical analysis.) This document is the statement of the task force…

Continue reading

Categories: ASA Task Force on Significance and Replicability, Schachtman, significance tests | 9 Comments

June 24: “Have Covid-19 lockdowns led to an increase in domestic violence? Drawing inferences from police administrative data” (Katrin Hohl)

The tenth meeting of our Phil Stat Forum*:

The Statistics Wars
and Their Casualties

24 June 2021

TIME: 15:00-16:45 (London); 10:00-11:45 (New York, EST)

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

Katrin Hohl_copy

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“Have Covid-19 lockdowns led to an increase in domestic violence? Drawing inferences from police administrative data” 

Katrin Hohl Continue reading

Categories: Error Statistics | Leave a comment

At long last! The ASA President’s Task Force Statement on Statistical Significance and Replicability

The ASA President’s Task Force Statement on Statistical Significance and Replicability has finally been published. It found a home in The Annals of Applied Statistics, after everyone else they looked to–including the ASA itself– refused to publish it.  For background see this post. I’ll comment on it in a later post. There is also an Editorial: Statistical Significance, P-Values, and Replicability by Karen Kafadar. Continue reading

Categories: ASA Task Force on Significance and Replicability | 9 Comments

June 24: “Have Covid-19 lockdowns led to an increase in domestic violence? Drawing inferences from police administrative data” (Katrin Hohl)

The tenth meeting of our Phil Stat Forum*:

The Statistics Wars
and Their Casualties

24 June 2021

TIME: 15:00-16:45 (London); 10:00-11:45 (New York, EST)

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

Katrin Hohl_copy

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“Have Covid-19 lockdowns led to an increase in domestic violence? Drawing inferences from police administrative data” 

Katrin Hohl Continue reading

Categories: Error Statistics | Leave a comment

The F.D.A.’s controversial ruling on an Alzheimer’s drug (letter from a reader)(ii)

I was watching Biogen’s stock (BIIB) climb over 100 points yesterday because its Alzheimer’s drug, aducanumab [brand name: Aduhelm], received surprising FDA approval.  I hadn’t been following the drug at all (it’s enough to try and track some Covid treatments/vaccines). I knew only that the FDA panel had unanimously recommended not to approve it last year, and the general sentiment was that it was heading for FDA rejection yesterday. After I received an email from Geoff Stuart[i] asking what I thought, I found out a bit more. He wrote: Continue reading

Categories: PhilStat/Med, preregistration | 10 Comments

Bayesian philosophers vs Bayesian statisticians: Remarks on Jon Williamson

While I would agree that there are differences between Bayesian statisticians and Bayesian philosophers, those differences don’t line up with the ones drawn by Jon Williamson in his presentation to our Phil Stat Wars Forum (May 20 slides). I hope Bayesians (statisticians, or more generally, practitioners, and philosophers) will weigh in on this. 

Continue reading
Categories: Phil Stat Forum, stat wars and their casualties | 11 Comments

Mayo Casualties of O-Bayesianism and Williamson response

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After Jon Williamson’s talk, Objective Bayesianism from a Philosophical Perspective, at the PhilStat forum on May 22, I raised some general “casualties” encountered by objective, non-subjective or default Bayesian accounts, not necessarily Williamson’s. I am pasting those remarks below, followed by some additional remarks and the video of his responses to my main kvetches. Continue reading

Categories: frequentist/Bayesian, objective Bayesians, Phil Stat Forum | 4 Comments

May 20: “Objective Bayesianism from a Philosophical Perspective” (Jon Williamson)

The ninth meeting of our Phil Stat Forum*:

The Statistics Wars
and Their Casualties

20 May 2021

TIME: 15:00-16:45 (London); 10:00-11:45 (New York, EST)

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

“Objective Bayesianism from a philosophical perspective” 

Jon Williamson Continue reading

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Tom Sterkenburg Reviews Mayo’s “Statistical Inference as Severe Testing: How to Get Beyond the Statistics Wars” (2018, CUP)

T. Sterkenburg

Tom Sterkenburg, PhD
Postdoctoral Fellow
Munich Center for Mathematical Philosophy
LMU Munich
Munich, German

Deborah G. Mayo: Statistical Inference as Severe Testing: How to Get Beyond the Statistics Wars

The foundations of statistics is not a land of peace and quiet. “Tribal warfare” is perhaps putting it too strong, but it is the case that for decades now various camps and subcamps have been exchanging heated arguments about the right statistical methodology. That these skirmishes are not just an academic exercise is clear from the widespread use of statistical methods, and contemporary challenges that cry for more secure foundations: the rise of big data, the replication crisis.

Continue reading

Categories: SIST, Statistical Inference as Severe Testing–Review, Tom Sterkenburg | 9 Comments

CUNY zoom talk on Wednesday: Evidence as Passing a Severe Test

If interested, write to me for the zoom link (error@vt.edu).

Categories: Announcement | Leave a comment

April 22 “How an information metric could bring truce to the statistics wars” (Daniele Fanelli)

The eighth meeting of our Phil Stat Forum*:

The Statistics Wars
and Their Casualties

22 April 2021

TIME: 15:00-16:45 (London); 10:00-11:45 (New York, EST)

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

“How an information metric could bring truce to the statistics wars

Daniele Fanelli Continue reading

Categories: Phil Stat Forum, replication crisis, stat wars and their casualties | Leave a comment

A. Spanos: Jerzy Neyman and his Enduring Legacy (guest post)

I am reblogging a guest post that Aris Spanos wrote for this blog on Neyman’s birthday some years ago.   

A. Spanos

A Statistical Model as a Chance Mechanism
Aris Spanos 

Jerzy Neyman (April 16, 1894 – August 5, 1981), was a Polish/American statistician[i] who spent most of his professional career at the University of California, Berkeley. Neyman is best known in statistics for his pioneering contributions in framing the Neyman-Pearson (N-P) optimal theory of hypothesis testing and his theory of Confidence Intervals. (This article was first posted here.) Continue reading

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Happy Birthday Neyman: What was Neyman opposing when he opposed the ‘Inferential’ Probabilists?

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Today is Jerzy Neyman’s birthday (April 16, 1894 – August 5, 1981). I’m posting a link to a quirky paper of his that explains one of the most misunderstood of his positions–what he was opposed to in opposing the “inferential theory”. The paper is Neyman, J. (1962), ‘Two Breakthroughs in the Theory of Statistical Decision Making‘ [i] It’s chock full of ideas and arguments. “In the present paper” he tells us, “the term ‘inferential theory’…will be used to describe the attempts to solve the Bayes’ problem with a reference to confidence, beliefs, etc., through some supplementation …either a substitute a priori distribution [exemplified by the so called principle of insufficient reason] or a new measure of uncertainty” such as Fisher’s fiducial probability. It arises on p. 391 of Excursion 5 Tour III of Statistical Inference as Severe Testing: How to Get Beyond the Statistics Wars (2018, CUP). Here’s a link to the proofs of that entire tour. If you hear Neyman rejecting “inferential accounts” you have to understand it in this very specific way: he’s rejecting “new measures of confidence or diffidence”. Here he alludes to them as “easy ways out”. He is not rejecting statistical inference in favor of behavioral performance as typically thought. Neyman always distinguished his error statistical performance conception from Bayesian and Fiducial probabilisms [ii]. The surprising twist here is semantical and the culprit is none other than…Allan Birnbaum. Yet Birnbaum gets short shrift, and no mention is made of our favorite “breakthrough” (or did I miss it?). You can find quite a lot on this blog searching Birnbaum. Continue reading

Categories: Bayesian/frequentist, Error Statistics, Neyman | 3 Comments

Intellectual conflicts of interest: Reviewers

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Where do journal editors look to find someone to referee your manuscript (in the typical “double blind” review system in academic journals)? One obvious place to look is the reference list in your paper. After all, if you’ve cited them, they must know about the topic of your paper, putting them in a good position to write a useful review. The problem is that if your paper is on a topic of ardent disagreement, and you argue in favor of one side of the debates, then your reference list is likely to include those with actual or perceived conflicts of interest. After all, if someone has a strong standpoint on an issue of some controversy, and a strong interest in persuading others to accept their side, it creates an intellectual conflict of interest, if that person has power to uphold that view. Since your referee is in a position of significant power to do just that, it follows that they have a conflict of interest (COI). A lot of attention is paid to author’s conflicts of interest, but little into intellectual or ideological conflicts of interests of reviewers. At most, the concern is with the reviewer having special reasons to favor the author, usually thought to be indicated by having been a previous co-author. We’ve been talking about journal editors conflicts of interest as of late (e.g., with Mark Burgman’s presentation at the last Phil Stat Forum) and this brings to mind another one. Continue reading

Categories: conflicts of interest, journal referees | 12 Comments

ASA to Release the Recommendations of its Task Force on Statistical Significance and Replication

The American Statistical Association has announced that it has decided to reverse course and share the recommendations developed by the ASA Task Force on Statistical Significance and Replicability in one of its official channels. The ASA Board created this group [1] in November 2019 “with a charge to develop thoughtful principles and practices that the ASA can endorse and share with scientists and journal editors.” (AMSTATNEWS 1 February 2020). Some members of the ASA Board felt that its earlier decision not to make these recommendations public, but instead to leave the group to publish its recommendations on its own, might give the appearance of a conflict of interest between the obligation of the ASA to represent the wide variety of methodologies used by its members in widely diverse fields, and the advocacy by some members who believe practitioners should stop using the term “statistical significance” and end the practice of using p-value thresholds in interpreting data [the Wasserstein et al. (2019) editorial]. I think that deciding to publicly share the new Task Force recommendations is very welcome, given especially that the Task Force was appointed to avoid just such an apparent conflict of interest. Past ASA President, Karen Kafadar noted: Continue reading

Categories: conflicts of interest | 1 Comment

The Stat Wars and Intellectual conflicts of interest: Journal Editors

 

Like most wars, the Statistics Wars continues to have casualties. Some of the reforms thought to improve reliability and replication may actually create obstacles to methods known to improve on reliability and replication. At each one of our meeting of the Phil Stat Forum: “The Statistics Wars and Their Casualties,” I take 5 -10 minutes to draw out a proper subset of casualties associated with the topic of the presenter for the day. (The associated workshop that I have been organizing with Roman Frigg at the London School of Economics (CPNSS) now has a date for a hoped for in-person meeting in London: 24-25 September 2021.) Of course we’re interested not just in casualties but in positive contributions, though what counts as a casualty and what a contribution is itself a focus of philosophy of statistics battles.

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Categories: Error Statistics | Leave a comment

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

The seventh meeting of our Phil Stat Forum*:

The Statistics Wars
and Their Casualties

25 March, 2021

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

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

How should applied science journal editors deal with statistical controversies?

Mark Burgman Continue reading

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

Pandemic Nostalgia: The Corona Princess: Learning from a petri dish cruise (reblog 1yr)

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Last week, giving a long postponed talk for the NY/NY Metro Area Philosophers of Science Group (MAPS), I mentioned how my book Statistical Inference as Severe Testing: How to Get Beyond the Statistics Wars (2018, CUP) invites the reader to see themselves on a special interest cruise as we revisit old and new controversies in the philosophy of statistics–noting that I had no idea in writing the book that cruise ships would themselves become controversial in just a few years. The first thing I wrote during early pandemic days last March was this post on the Diamond Princess. The statistics gleaned from the ship remain important resources which haven’t been far off in many ways. I reblog it here. Continue reading

Categories: covid-19, memory lane | Leave a comment

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