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). Continue reading →
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 →
Stephen Senn Consultant Statistician
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  (P108)
In a previous blog https://www.linkedin.com/pulse/cause-concern-stephen-senn/ I considered Lord’s Paradox, 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  and also discussed in The Book of Why. 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 →
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 →
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 (co-author Mike Tamir, Berkeley) on November 13, 9 am-12:15 pm
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.
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.
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.
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. 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 →
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 →
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.
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)
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)
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).
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  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
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…
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 →
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.
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 →