Just as in the past 5 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 2017? Anyway, it’s 6 hrs later here, so I’m about to leave for that spot in the road… If I’m picked up, I’ll add an update at the end.
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, 2016) 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. Continue reading
When logical fallacies of statistics go uncorrected, they are repeated again and again…and again. And so it is with the limb-sawing fallacy I first posted in one of my “Overheard at the Comedy Hour” posts.* It now resides as a comic criticism of significance tests in a paper by Szucs and Ioannidis (posted this week), Here’s their version:
“[P]aradoxically, when we achieve our goal and successfully reject H0 we will actually be left in complete existential vacuum because during the rejection of H0 NHST ‘saws off its own limb’ (Jaynes, 2003; p. 524): If we manage to reject H0then it follows that pr(data or more extreme data|H0) is useless because H0 is not true” (p.15).
Here’s Jaynes (p. 524):
“Suppose we decide that the effect exists; that is, we reject [null hypothesis] H0. Surely, we must also reject probabilities conditional on H0, but then what was the logical justification for the decision? Orthodox logic saws off its own limb.’ “
Ha! Ha! By this reasoning, no hypothetical testing or falsification could ever occur. As soon as H is falsified, the grounds for falsifying disappear! If H: all swans are white, then if I see a black swan, H is falsified. But according to this criticism, we can no longer assume the deduced prediction from H! What? Continue reading
3 years ago…
MONTHLY MEMORY LANE: 3 years ago: December 2013. I mark in red three posts from each month that seem most apt for general background on key issues in this blog, excluding those reblogged recently, and in green up to 3 others I’d recommend. Posts that are part of a “unit” or a group count as one. In this post, that makes 12/27-12/28 count as one.
- (12/3) Stephen Senn: Dawid’s Selection Paradox (guest post)
- (12/7) FDA’s New Pharmacovigilance
- (12/9) Why ecologists might want to read more philosophy of science (UPDATED)
- (12/11) Blog Contents for Oct and Nov 2013
- (12/14) The error statistician has a complex, messy, subtle, ingenious piece-meal approach
- (12/15) Surprising Facts about Surprising Facts
- (12/19) A. Spanos lecture on “Frequentist Hypothesis Testing”
- (12/24) U-Phil: Deconstructions [of J. Berger]: Irony & Bad Faith 3
- (12/25) “Bad Arguments” (a book by Ali Almossawi)
- (12/26) Mascots of Bayesneon statistics (rejected post)
- (12/27) Deconstructing Larry Wasserman
- (12/28) More on deconstructing Larry Wasserman (Aris Spanos)
- (12/28) Wasserman on Wasserman: Update! December 28, 2013
- (12/31) Midnight With Birnbaum (Happy New Year)
 Monthly memory lanes began at the blog’s 3-year anniversary in Sept, 2014.
 New Rule, July 30, 2016-very convenient.
Placebos: it’s not only the patients that are fooled
Head of Competence Center for Methodology and Statistics (CCMS)
Luxembourg Institute of Health
In my opinion a great deal of ink is wasted to little purpose in discussing placebos in clinical trials. Many commentators simply do not understand the nature and purpose of placebos. To start with the latter, their only purpose is to permit blinding of treatments and, to continue to the former, this implies that their nature is that they are specific to the treatment studied.
Consider an example. Suppose that Pannostrum Pharmaceuticals wishes to prove that its new treatment for migraine, Paineaze® (which is in the form of a small red circular pill) is superior to the market-leader offered by Allexir Laboratories, Kalmer® (which is a large purple lozenge). Pannostrum decides to do a head-to head comparison and of course, therefore will require placebos. Every patient will have to take a red pill and a purple lozenge. In the Paineaze arm what is red will be Paineaze and what is purple ‘placebo to Kalmer’. In the Kalmer arm what is red will be ‘placebo to Paineaze’ and what is purple will be Kalmer.
I came across a paper, “Tests of Statistical Significance Made Sound,” by Brian Haig, a psychology professor at the University of Canterbury, New Zealand. It hits most of the high notes regarding statistical significance tests, their history & philosophy and, refreshingly, is in the error statistical spirit! I’m pasting excerpts from his discussion of “The Error-Statistical Perspective”starting on p.7.
The Error-Statistical Perspective
An important part of scientific research involves processes of detecting, correcting, and controlling for error, and mathematical statistics is one branch of methodology that helps scientists do this. In recognition of this fact, the philosopher of statistics and science, Deborah Mayo (e.g., Mayo, 1996), in collaboration with the econometrician, Aris Spanos (e.g., Mayo & Spanos, 2010, 2011), has systematically developed, and argued in favor of, an error-statistical philosophy for understanding experimental reasoning in science. Importantly, this philosophy permits, indeed encourages, the local use of ToSS, among other methods, to manage error. Continue reading