Posts Tagged With: error statistical philosophy

Bad news bears: ‘Bayesian bear’ rejoinder-reblog mashup

Oh No! It’s those mutant bears again. To my dismay, I’ve been sent, for the third time, that silly, snarky, adolescent, clip of those naughty “what the p-value” bears (first posted on Aug 5, 2012), who cannot seem to get a proper understanding of significance tests into their little bear brains. So apparently some people haven’t seen my rejoinder which, as I said then, practically wrote itself. So since it’s Saturday night here at the Elbar Room, let’s listen in to a mashup of both the clip and my original rejoinder (in which p-value bears are replaced with hypothetical Bayesian bears). 

These stilted bear figures and their voices are sufficiently obnoxious in their own right, even without the tedious lampooning of p-values and the feigned horror at learning they should not be reported as posterior probabilities.

Mayo’s Rejoinder:

Bear #1: Do you have the results of the study?

Bear #2:Yes. The good news is there is a .996 probability of a positive difference in the main comparison.

Bear #1: Great. So I can be well assured that there is just a .004 probability that such positive results would occur if they were merely due to chance.

Bear #2: Not really, that would be an incorrect interpretation. Continue reading

Categories: Bayesian/frequentist, Comedy, P-values, Statistics | Tags: , , ,

Error Statistics (brief overview)

In view of some questions about “behavioristic” vs “evidential” construals of frequentist statistics (from the last post), and how the error statistical philosophy tries to improve on Birnbaum’s attempt at providing the latter, I’m reblogging a portion of a post from Nov. 5, 2011 when I also happened to be in London. (The beginning just records a goofy mishap with a skeletal key, and so I leave it out in this reblog.) Two papers with much more detail are linked at the end.

Error Statistics

(1) There is a “statistical philosophy” and a philosophy of science. (a) An error-statistical philosophy alludes to the methodological principles and foundations associated with frequentist error-statistical methods. (b) An error-statistical philosophy of science, on the other hand, involves using the error-statistical methods, formally or informally, to deal with problems of philosophy of science: to model scientific inference (actual or rational), to scrutinize principles of inference, and to address philosophical problems about evidence and inference (the problem of induction, underdetermination, warranting evidence, theory testing, etc.). Continue reading

Categories: Error Statistics, Philosophy of Statistics, Statistics | Tags: , ,

Seminars at the London School of Economics: Contemporary Problems in Philosophy of Statistics

As a visitor of the Centre for Philosophy of Natural and Social Science (CPNSS) at the London School of Economics and Political Science, I am leading 3 seminars in the department of Philosophy, Logic, and Scientific Method on Wednesdays from Nov. 28-Dec 12 on Contemporary Philosophy of Statistics under the PH500 rubric, Room: Lak 2.06 (Lakatos building). Interested individuals who have not yet contacted me, write: .*
The Autumn seminars will also feature discussions with distinguished guest statisticians: Sir David Cox (Oxford); Dr. Stephen Senn: (Competence Center for Methodology and Statistics, Luxembourg); Dr. Christian Hennig (University College, London):
  • 28 November: (10 – 12 noon): Mayo: On Birnbaum’s argument for the Likelihood Principle: A 50-year old error and its influence on statistical foundations (See my blog and links within.)

5 December and 12 December: Statistical Science meets philosophy of science: Mayo and guests:

  • 5 Dec: 12 (noon)- 2p.m.: Sir David Cox
  • 12 Dec (10-12).Dr. Stephen Senn;
    Dr. Christian Hennig: TBA

Topics, activities, readings :TBA (Two 2012 Summer Seminars may be found here).

Blurb: Debates over the philosophical foundations of statistical science have a long and fascinating history marked by deep and passionate controversies that intertwine with fundamental notions of the nature of statistical inference and the role of probabilistic concepts in inductive learning. Progress in resolving decades-old controversies which still shake the foundations of statistics, demands both philosophical and technical acumen, but gaining entry into the current state of play requires a roadmap that zeroes in on core themes and current standpoints. While the seminar will attempt to minimize technical details, it will be important to clarify key notions to fully contribute to the debates. Relevance for general philosophical problems will be emphasized. Because the contexts in which statistical methods are most needed are ones that compel us to be most aware of strategies scientists use to cope with threats to reliability, considering the nature of statistical method in the collection, modeling, and analysis of data is an effective way to articulate and warrant general principles of evidence and inference.
Room 2.06 Lakatos Building; Centre for Philosophy of Natural and Social Science
 London School of Economics
 Houghton Street
London WC2A 2AE
Administrator: T. R.

For  updates, details, and associated readings: please check the LSE Ph500 page on my blog or write to me.
*It is not necessary to have attended the 2 sessions held during the summer of 2012.

Categories: Announcement, philosophy of science, Statistics | Tags: ,

A “Bayesian Bear” rejoinder practically writes itself…

These stilted bear figures and their voices are sufficiently obnoxious in their own right, even without the tedious lampooning of p-values and the feigned horror at learning they should not be reported as posterior probabilities. Coincidentally, I have been sent several different p-value U-Tube clips in the past two weeks, rehearsing essentially the same interpretive issues, but this one (“what the p-value”*) was created by some freebee outfit that will apparently set their irritating cartoon bear voices to your very own dialogue (I don’t know the website or outfit.)

The presumption is that somehow there would be no questions or confusion of interpretation were the output in the form of a posterior probability. The problem of indicating the extent of discrepancies that are/are not warranted by a given p-value is genuine but easy enough to solve**. What I never understand is why it is presupposed that the most natural and unequivocal way to interpret and communicate evidence (in this case, leading to low p-values) is by means of a (posterior) probability assignment, when it seems clear that the more relevant question the testy-voiced (“just wait a tick”) bear would put to the know-it-all bear would be: how often would this method erroneously declare a genuine discrepancy? A corresponding “Bayesian bear” video practically writes itself, but I’ll let you watch this first. Share any narrative lines that come to mind.

*Reference: Blume, J. and J. F. Peipert (2003). “What your statistician never told you about P-values.” J Am Assoc Gynecol Laparosc 10(4): 439-444.

**See for example, Mayo & Spanos (2011) ERROR STATISTICS

Categories: Statistics | Tags: , , ,

Metablog: May 31, 2012

Dear Reader: I will be traveling a lot in the next few weeks, and may not get to post much; we’ll see. If I do not reply to comments, I’m not ignoring them—they’re a lot more fun than some of the things I must do now to complete my book, but need to resist, especially while traveling and giving seminars.* The  rule we’ve followed is for comments to shut after 10 days, but we wanted to allow them still to appear. The blogpeople on Elba forward comments for 10 days, so beyond that it’s just haphazard if I notice them. It’s impossible otherwise to keep this blog up at all, and I would like to. Feel free to call any to my attention (use “can we talk” page or If there’s a burning issue,  interested readers might wish to poke around (or scour) the multiple layers of goodies on the left hand side of this web page, wherein all manner of foundational/statistical controversies are considered from many years of working in this area. In a recent attempt by Aris Spanos and I to address the age-old criticisms from the perspective of the “error statistical philosophy,” we delineate  13 criticisms.  I list them below. Continue reading

Categories: Metablog, Philosophy of Statistics, Statistics | Tags: , ,

LSE Summer Seminar: Contemporary Problems in Philosophy of Statistics

As a visitor of the Centre for Philosophy of Natural and Social Science (CPNSS) at the London School of Economics and Political Science, I am planning to lead 5 seminars in the department of Philosophy, Logic, and Scientific Method this summer (2) and autumn (3) on Contemporary Philosophy of Statistics under the PH500 rubric, (listed under summer term).   This will be rather informal, based on the book I am writing with this name. There will be at least one guest seminar leader in the fall. Anyone interested in attending or finding out more may write to me: .*

Wednesday   6th June            3-5pm                        T206

Wednesday 13th June             3-5pm                        T206

Autumn term dates: To Be Announced

LSE contact

PH 500. Contemporary Problems in Philosophy of Statistical Science Continue reading

Categories: Announcement, philosophy of science, Statistics | Tags: ,

Jean Miller: Happy Sweet 16 to EGEK #2 (Hasok Chang Review of EGEK)

Jean Miller here, reporting back from the island. Tonight we complete our “sweet sixteen” celebration of Mayo’s EGEK (1996) with the book review by Dr. Hasok Chang (currently the Hans Rausing Professor of History and Philosophy of Science at the University of Cambridge). His was chosen as our top favorite in the category of ‘reviews by philosophers’. Enjoy!

REVIEW: British Journal of the Philosophy of Science 48 (1997), 455-459
DEBORAH MAYO Error and the Growth of Experimental Knowledge, 
The University of Chicago Press, 1996
By: Hasok Chang

Deborah Mayo’s Error and the Growth of Experimental Knowledge is a rich, useful, and accessible book. It is also a large volume which few people can realistically be expected to read cover to cover. Considering those factors, the main focus of this review will be on providing various potential readers with guidelines for making the best use of the book.

As the author herself advises, the main points can be grasped by reading the first and the last chapters. The real benefit, however, would only come from studying some of the intervening chapters closely. Below I will offer comments on several of the major strands that can be teased apart, though they are found rightly intertwined in the book. Continue reading

Categories: philosophy of science, Statistics | Tags: , , ,

Jean Miller: Happy Sweet 16 to EGEK! (Shalizi Review: “We Have Ways of Making You Talk”)

Jean Miller here.  (I obtained my PhD with D. Mayo in Phil/STS at VT.) Some of us “island philosophers” have been looking to pick our favorite book reviews of EGEK (Mayo 1996; Lakatos Prize 1999) to celebrate its “sweet sixteen” this month. This review, by Dr. Cosma Shalizi (CMU, Stat) has been chosen as the top favorite (in the category of reviews outside philosophy).  Below are some excerpts–it was hard to pick, as each paragraph held some new surprise, or unique way to succinctly nail down the views in EGEK. You can read the full review here. Enjoy.

“We Have Ways of Making You Talk, or, Long Live Peircism-Popperism-Neyman-Pearson Thought!”
by Cosma Shalizi

After I’d bungled teaching it enough times to have an idea of what I was doing, one of the first things students in my introductory physics classes learned (or anyway were taught), and which I kept hammering at all semester, was error analysis: estimating the uncertainty in measurements, propagating errors from measured quantities into calculated ones, and some very quick and dirty significance tests, tests for whether or not two numbers agree, within their associated margins of error. I did this for purely pragmatic reasons: it seemed like one of the most useful things we were supposed to teach, and also one of the few areas where what I did had any discernible effect on what they learnt. Now that I’ve read Mayo’s book, I’ll be able to offer another excuse to my students the next time I teach error analysis, namely, that it’s how science really works.

I exaggerate her conclusion slightly, but only slightly. Mayo is a dues-paying philosopher of science (literally, it seems), and like most of the breed these days is largely concerned with questions of method and justification, of “ampliative inference” (C. S. Peirce) or “non-demonstrative inference” (Bertrand Russell). Put bluntly and concretely: why, since neither can be deduced rigorously from unquestionable premises, should we put more trust in David Grinspoon‘s ideas about Venus than in those of Immanuel Velikovsky? A nice answer would be something like, “because good scientific theories are arrived at by employing thus-and-such a method, which infallibly leads to the truth, for the following self-evident reasons.” A nice answer, but not one which is seriously entertained by anyone these days, apart from some professors of sociology and literature moonlighting in the construction of straw men. In the real world, science is alas fallible, subject to constant correction, and very messy. Still, mess and all, we somehow or other come up with reliable, codified knowledge about the world, and it would be nice to know how the trick is turned: not only would it satisfy curiosity (“the most agreeable of all vices” — Nietzsche), and help silence such people as do, in fact, prefer Velikovsky to Grinspoon, but it might lead us to better ways of turning the trick. Asking scientists themselves is nearly useless: you’ll almost certainly just get a recital of whichever school of methodology we happened to blunder into in college, or impatience at asking silly questions and keeping us from the lab. If this vice is to be indulged in, someone other than scientists will have to do it: namely, the methodologists. Continue reading

Categories: philosophy of science, Statistics | Tags: , , , ,

MetaBlog: March 2, 2012

old blogspot typewriterDear Reader: I’ll be traveling, mostly to London, for a couple of weeks, but plan to keep up the blog as usual (semi-irratically regular*); I will mostly keep msc meanderings under the wraps of “pages” (I don’t know if anyone ever reads them, I’m still trying to figure them out actually.)

I will be giving a Popper Lecture at the LSE on Tuesday March 6**.  It’s on the philosophy of experiment, no direct discussion of PhilStat; however, I’ve reserved a space Wednesday March 7, mid-day, for anyone who wants to meet to talk about recent PhilStat ponderings, the business on the strong LP, and related issues. If you’re in the neighborhood, write and I’ll give particulars,

Continue reading

Categories: Metablog | Tags: , ,

"Philosophy of Statistics": Nelder on Lindley

A friend from Elba surprised me by sending the interesting paper and discussion of Dennis Lindley (2000), “The Philosophy of Statistics,” which I hadn’t seen in years.  She suggested, as especially apt, J. Nelder’s remarks; I recommend the full article and discussion:
(from) Comments by J. Nelder:

Recently (Nelder,1999) I have argued that statistics should be called statistical science, and that probability theory should be called statistical mathematics (not mathematical statistics). I think that Professor Lindley’s paper should be called the philosophy of statistical mathematics, and within it there is little that I disagree with. However, my interest is in the philosophy of statistical science, which I regard as different.  Statistical science is not just about the study of uncertainty but rather deals with inferences about scientific theories from uncertain data. Continue reading

Categories: Statistics | Tags: , ,

Skeleton Key and Skeletal Points for (Esteemed) Ghost Guest

Secret Key

Why attend presentations of interesting papers or go to smashing London sites when you can spend better than an hour racing from here to there because the skeleton key to your rented flat won’t turn the lock (after working fine for days)? [3 other neighbors tried, by the way, it wasn’t just me.] And what are the chances of two keys failing, including the porter’s key, and then a third key succeeding–a spare I’d never used but had placed in a hollowed-out volume of Error and Inference, and kept in an office at the London School of Economics?  (Yes, that is what the photo is!  A anonymous e-mailer guessed it right, so they must have spies!)  As I ran back and forth one step ahead of the locksmith, trying to ignore my still-bum knee (I left the knee brace in the flat) and trying not to get run over—not easy, in London, for me—I mulled over the perplexing query from one of my Ghost Guests (who asked for my positive account). Continue reading

Categories: philosophy of science, Statistics | Tags: , ,

A Highly Anomalous Event

The journey to San Francisco was smooth sailing with no plane delays; within two hours of landing I found myself in the E.R. of St. Francis Hospital (with the philosopher of science Ronald Giere), unable to walk.  I have just described an unexpected, “anomalous”, highly unusual event, but no one would suppose it was anomalous FOR, i.e., evidence against some theory, say, in molecular biology.  Yet I am  getting e-mails (from readers) saying, in effect, that since the improbable coin toss result is very unexpected/anomalous in its own right, it therefore is anomalous for any and all theories, which is patently absurd.  What had happened, in case you want to know, is that just as I lunged forward to grab my (bulging) suitcase off the airline baggage thingy, out of the corner of my eye I saw my computer bag being pulled away by someone on my left, and as I simultaneously yanked it back, I tumbled over—very gently it seemed– twisting my knee in a funny way.  To my surprise/alarm, much as a tried, I could put no weight on my right leg without succumbing to a Geppeto-puppet-like collapse.  The event, of course, could rightly be regarded as anomalous for hypotheses about my invulnerability to such mishaps, because it runs counter to them.  I will assume this issue is now settled for our discussions, yes?

Categories: Statistics | Tags: , , , ,

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