Our presentations from the PSA: Philosophy in Science (PinS) symposium

Philosophy in Science:
Can Philosophers of Science Contribute to Science?

 

Below are the presentations from our remote session on “Philosophy in Science”on November 13, 2021 at the Philosophy of Science Association meeting. We are having an extended discussion on Monday November, 22 at 3pm Eastern Standard Time. If you wish to take part, write to me of your interest by email (error) with the subject “PinS” or use comments below. (Include name, affiliation and email).

Session Abstract: Although the question of what philosophy can bring to science is an old topic, the vast majority of current philosophy of science is a meta-discourse on science, taking science as its object of study, rather than an attempt to intervene on science itself. In this symposium, we discuss a particular interventionist approach, which we call “philosophy in science (PinS)”, i.e., an attempt at using philosophical tools to make a significant scientific contribution. This approach remains rare, but has been very successful in a number of cases, especially in philosophy of biology, medicine, physics, statistics, and the social sciences. Our goal is to provide a description of PinS through both a bibliometric approach and the examination of specific case studies. We also aim to explain how PinS differs from mainstream philosophy of science and partly similar approaches such as “philosophy of science in practice”.

Here are the members and the titles of their talks. (Link to session/abstracts):

  • Thomas Pradeu (CNRS & University Of Bordeaux) & Maël Lemoine (University Of Bordeaux): Philosophy in Science: Definition and Boundaries
  • Deborah Mayo (Virginia Tech): My Philosophical Interventions in Statistics
  • Elliott Sober (University Of Wisconsin – Madison): Philosophical Interventions in Science – a Strategy and a Case Study (Parsimony)
  • Randolph Nesse (Arizona State University) & Paul Griffiths (University of Sydney): How Evolutionary Science and Philosophy Can Collaborate to Redefine Disease

 

T. Pradeu & M. Lemoine slides: “Philosophy in Science: Definition and Boundaries”:

 

D. Mayo slides: “Philosophical Interventions in the Statistics Wars”:

 

E. Sober: “Philosophical Interventions in Science – A Strategy and a Case Study (Parsimony)”

 

R. Nesse & P. Griffiths: How Evolutionary Science and Philosophy Can Collaborate to Redefine Disease”:

Categories: PSA 2021 | 6 Comments

Our session is now remote: Philo of Sci Association (PSA): Philosophy IN Science (PinS): Can Philosophers of Science Contribute to Science?

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Philosophy in Science: Can Philosophers of Science Contribute to Science?
     on November 13, 2-4 pm

 

OUR SESSION HAS BECOME REMOTE: PLEASE JOIN US on ZOOM! This session revolves around the intriguing question: Can Philosophers of Science Contribute to Science? They’re calling it philosophy “in” science–when philosophical ministrations actually intervene in a science itself.  This is the session I’ll be speaking in. I hope you will come to our session if you’re there–it’s hybrid, so you can’t see it through a remote link. But I’d like to hear what you think about this question–in the comments to this post. Continue reading

Categories: Announcement, PSA 2021 | Leave a comment

S. Senn: The Many Halls Problem (Guest Post)

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Stephen Senn
Consultant Statistician
Edinburgh, Scotland

 

The Many Halls Problem
It’s not that paradox but another

Generalisation is passing…from the consideration of a restricted set to that of a more comprehensive set containing the restricted one…Generalization may be useful in the solution of problems. George Pólya [1] (P108)

Introduction

In a previous blog  https://www.linkedin.com/pulse/cause-concern-stephen-senn/ I considered Lord’s Paradox[2], applying John Nelder’s calculus of experiments[3, 4]. Lord’s paradox involves two different analyses of the effect of two different diets, one for each of two different student halls, on weight of students. One statistician compares the so-called change scores or gain scores (final weight minus initial weight) and the other compares final weights, adjusting for initial weights using analysis of covariance. Since the mean initial weights vary between halls, the two analyses will come to different conclusions unless the slope of final on initial weights just happens to be one (in practice, it would usually be less). The fact that two apparently reasonable analyses would lead to different conclusions constitutes the paradox. I chose the version of the paradox outlined by Wainer and Brown [5] and also discussed in The Book of Why[6].  I illustrated this by considering two different experiments: one in which, as in the original example, the diet varies between halls and a further example in which it varies within halls. I simulated some data which are available in the appendix to that blog but which can also be downloaded from here http://www.senns.uk/Lords_Paradox_Simulated.xls so that any reader who wishes to try their hand at analysis can have a go. Continue reading

Categories: Lord's paradox, S. Senn | 7 Comments

I’ll be speaking at the Philo of Sci Association (PSA): Philosophy IN Science: Can Philosophers of Science Contribute to Science?

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Philosophy in Science: Can Philosophers of Science Contribute to Science?
     on November 13, 2-4 pm

 

This session revolves around the intriguing question: Can Philosophers of Science Contribute to Science? They’re calling it philosophy “in” science–when philosophical ministrations actually intervene in a science itself.  This is the session I’ll be speaking in. I hope you will come to our session if you’re there–it’s hybrid, so you can’t see it through a remote link. But I’d like to hear what you think about this question–in the comments to this post. Continue reading

Categories: Error Statistics | 4 Comments

Philo of Sci Assoc (PSA) Session: Current Debates on Statistical Modeling and Inference

 

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The Philosophy of Science Association (PSA) is holding its biennial meeting (one year late)–live/hybrid/remote*–in November, 2021, and I plan to be there (first in-person meeting since Feb 2020). Some of the members from the 2019 Summer Seminar that I ran with Aris Spanos are in a Symposium:

Current Debates on Statistical Modeling and Inference
     on November 13, 9 am-12:15 pm  

Here are the members and talks (Link to session/abstracts):

  • Aris Spanos (Virginia Tech): Self-Correction and Statistical Misspecification (co-author Deborah Mayo (Virginia Tech)
  • Roubin Gong (Rutgers): Measuring Severity in Statistical Inference
  • Riet van Bork (University of Amsterdam): Psychometric Models: Statistics and Interpretation (co-author Jan-Willem Romeijn (University of Groningen)
  • Marcello di Bello (Lehman College CUNY): Is Algorithmic Fairness Possible?
  • Elay Shech (Auburn University): Statistical Modeling, Mis-specification Testing, and Exploration
Continue reading
Categories: Error Statistics | 1 Comment

The (Vaccine) Booster Wars: A prepost

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We’re always reading about how the pandemic has created a new emphasis on preprints, so it stands to reason that non-reviewed preposts would now have a place in blogs. Maybe then I’ll “publish” some of the half-baked posts languishing on draft in errorstatistics.com. I’ll update or replace this prepost after reviewing.

The Booster wars

Continue reading

Categories: the (Covid vaccine) booster wars | 18 Comments

Workshop-New Date!

The Statistics Wars
and Their Casualties

New Date!

4-5 April 2022

London School of Economics (CPNSS)

Yoav Benjamini (Tel Aviv University), Alexander Bird (University of Cambridge), Mark Burgman (Imperial College London),  Daniele Fanelli (London School of Economics and Political Science), Roman Frigg (London School of Economics and Political Science), Stephen Guettinger (London School of Economics and Political Science), David Hand (Imperial College London), Margherita Harris (London School of Economics and Political Science), Christian Hennig (University of Bologna), Katrin Hohl (City University London), Daniël Lakens (Eindhoven University of Technology), Deborah Mayo (Virginia Tech), Richard Morey (Cardiff University), Stephen Senn (Edinburgh, Scotland), Jon Williamson (University of Kent) Continue reading

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All She Wrote (so far): Error Statistics Philosophy: 10 years on

Dear Reader: I began this blog 10 years ago (Sept. 3, 2011)! A double celebration is taking place at the Elbar Room–remotely for the first time due to Covid– both for the blog and the 3 year anniversary of the physical appearance of my book: Statistical Inference as Severe Testing: How to Get Beyond the Statistics Wars [SIST] (CUP, 2018). A special rush edition made an appearance on Sept 3, 2018 in time for the RSS meeting in Cardiff, where we had a session deconstructing the arguments against statistical significance tests (with Sir David Cox, Richard Morey and Aris Spanos). Join us between 7 and 8 pm in a drink of Elba Grease.

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Many of the discussions in the book were importantly influenced (corrected and improved) by reader’s comments on the blog over the years. I posted several excerpts and mementos from SIST here. I thank readers for their input. Readers might want to look up the topics in SIST on this blog to check out the comments, and see how ideas were developed, corrected and turned into “excursions” in SIST.

I recently invited readers to weigh in on the ASA Task Force on Statistical significance and Replication--any time through September–to be part of a joint guest post (or posts). All contributors will get a free copy of SIST. Continue reading

Categories: 10 year memory lane, Statistical Inference as Severe Testing | Leave a comment

Should Bayesian Clinical Trialists Wear Error Statistical Hats? (i)

 

I. A principled disagreement

The other day I was in a practice (zoom) for a panel I’m in on how different approaches and philosophies (Frequentist, Bayesian, machine learning) might explain “why we disagree” when interpreting clinical trial data. The focus is radiation oncology.[1] An important point of disagreement between frequentist (error statisticians) and Bayesians concerns whether and if so, how, to modify inferences in the face of a variety of selection effects, multiple testing, and stopping for interim analysis. Such multiplicities directly alter the capabilities of methods to avoid erroneously interpreting data, so the frequentist error probabilities are altered. By contrast, if an account conditions on the observed data, error probabilities drop out, and we get principles such as the stopping rule principle. My presentation included a quote from Bayarri and J. Berger (2004): Continue reading

Categories: multiple testing, statistical significance tests, strong likelihood principle | 26 Comments

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. Yes, i know I’ve been neglecting this blog as of late, but this topic will appear in a new guise in a post I’m writing now, to appear tomorrow.

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.  Continue reading

Categories: E.S. Pearson, Error Statistics | 2 Comments

Fair shares: sexual justice in patient recruitment in clinical trials

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Stephen Senn
Consultant Statistician
Edinburgh, Scotland

It is hard to argue against the proposition that approaches to clinical research should treat not only men but also women fairly, and of course this applies also to other ways one might subdivide patients. However, agreeing to such a principle is not the same as acting on it and when one comes to consider what in practice one might do, it is far from clear what the principle ought to be. In other words, the more one thinks about implementing such a principle the less obvious it becomes as to what it is.

Three possible rules

Continue reading

Categories: evidence-based policy, PhilPharma, RCTs, S. Senn | 5 Comments

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)

Continue reading

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 | 2 Comments

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 | 10 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

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