Error and Inference [#1]
“The philosophy of science offer valuable tools for understanding and advancing solutions to the problems of evidence and inference in practice”—D. Mayo & A. Spanos, p.xiv, Error and Inference, 2010
Deborah Mayo kindly sent me her last book, whose subtitle is “Recent exchanges on experimental reasoning, reliability, and the objectivity and rationality of Science” and contributors are P. Achinstein, A. Chalmers, D. Cox, C. Glymour, L. Laudan, A. Musgrave, and J. Worrall, plus both editors, Deborah Mayo and Aris Spanos. Deborah Mayo rightly inferred that this debate was bound to appeal to my worries about the nature of testing and model choice and to my layman interest in the philosophy of Science. Speaking of which [layman], the book reads really well, even though I am missing references. And even though it cannot be read under my cherry tree (esp. now that weather has moved from été to étaumne… as I heard this morning on the national public radio) Deborah Mayo is clearly the driving force in putting this volume together, from setting the ERROR 06 conference to commenting the chapters of all contributors (but her own and Aris Spanos’). Her strongly frequentist perspective on the issues of testing and model choice are thus reflected in the overall tone of the volume, even though contributors bring some contradiction to the debate. (disclaimer: I found the comics below on Zoltan Dienes’s webpage. I however have no information nor opinion [yet] about the contents of the corresponding book.)
“However, scientists wish to resist relativistic, fuzzy, or post-modern turns (…) Notably, the Popperian requirement that our theories are testable and falsifiable is widely regarded to contain important insights about responsibile science and objectivity.“—D. Mayo & A. Spanos, p.2, Error and Inference, 2010
Given the philosophical, complex, and interesting nature of the work, I will split my comments into several linear posts (hence the #1), as I did for Evidence and Evolution. The following comments are thus about a linear (even pedestrian) and incomplete read through the first three chapters. Those comments are not pretending at any depth, but simply reflect the handwritten notes and counterarguments I scribbled as I was reading through… A complete book review was published in the Notre-Dame Philosophical Reviews. (Though, can you trust a review considering Sartre as a major philosopher?! At least, he appears as a counterpart to Bertrand Russell in the frontispiece of the review.) As illustrated by the above quote (which first part I obviously endorse), the overall perspective in the book is Popperian, despite Popper’s criticism of statistical inference as a whole and of Bayesian statistics as a particular (although Andrew would disagree). Another fundamental concept throughout the book is the “Error-Statistical philosophy” whose Deborah Mayo is the proponent. One of the tenets of this philosophy is a reliance on statistical significance tests in the Fisher-Neyman-Pearson (or frequentist) tradition, along with a severity principle (“We want hypotheses that will allow for stringent testing so that if they pass we have evidence of a genuine experimental effect“, p.19) stated as (p.22)
A hypothesis H passes a severe test T with data x is
- x agrees with H, and
- with very high probability, test T would have produced a result that accords less well with H than does x, if H were false or incorrect.
(The p-value is advanced as a direct accomplishment of this goal, but I fail to see why it does or why a Bayes factor would not. Indeed, the criterion depends on the definition of probability when H is false or incorrect. This relates to Mayo’s criticism of the Bayesian approach, as explained below.)
“Formal error-statistical tests provide tools to ensure that errors will be correctly detected with high probabilities“—D. Mayo, p.33, Error and Inference, 2010
In Chapter 1, Deborah Mayo has a direct go at the Bayesian approach. The main criticism is about the Bayesian approach to testing (defined through the posterior probability of the hypothesis, rather than through the predictive) is about the catchall hypothesis, a somehow desultory term replacing the alternative hypothesis. According to Deborah Mayo, this alternative should “include all possible rivals, including those not even though of” (p.37). This sounds like a weak argument, although it was also used by Alan Templeton in his rebuttal of ABC, given that (a) it should also apply in the frequentist sense, in order to define the probability distribution “when H is false or incorrect” (see, e.g., “probability of so good an agreement (between H and x) calculated under the assumption that H is false”, p.40); (b) a well-defined alternative should be available as testing an hypothesis is very rarely the end of the story: if H is rejected, there should/will be a contingency plan; (c) rejecting or accepting an hypothesis H in terms of the sole null hypothesis H does not make sense from operational as well as from game-theoretic perspectives. The further argument that the posterior probability of H is a direct function of the prior probability of H does not stand against the Bayes factor. (The same applies to the criticism that the Bayesian approach does not accommodate newcomers, i.e., new alternatives.) Stating that “one cannot vouch for the reliability of [this Bayesian] procedure—that it would rarely affirm theory T were T false” (p.37) completely ignores the wealth of results about the consistency of the Bayes factor (since the “asymptotic long run”, p.20, matters in the Error-Statistical philosophy). The final argument that Bayesians rank “theories that fit the data equally well (i.e., have identical likelihoods)” (p.38) does not account for (or dismisses, p.50, referring to Jeffreys and Berger instead of Jefferys and Berger) the fact that Bayes factors are automated Occam’s razors in that the averaging of the likelihoods over spaces of different dimensions are natural advocates of simpler models. Even though I plan to discuss this point in a second post, Deborah Mayo also seems to imply that Bayesians are using the data twice (this is how I interpret the insistance on same p. 50), which is a sin [genuine] Bayesian analysis can hardly be found guilty of!
As pointed out by Adam La Caze in Notre-Dame Philosophical Reviews:
An exchange on Bayesian philosophy of science or Bayesian statistics would have been a welcome addition and would have benefited the dual goals of the volume. Bayesian philosophy of science and Bayesian statistics are a constant foil to Mayo’s work, but neither approach is given much of a voice. An exchange on Bayesian philosophy of science is made all the more relevant by the strength of Mayo’s challenge to a Bayesian account of theory appraisal. A virtue of the error-statistical account is its ability to capture the kind of detailed arguments that scientists make about data and the methods they employ to arrive at reliable inferences. Mayo clearly thinks that Bayesians are unable to supplement their view with any sort of prospective account of such methods. This seems contrary to practice where scientists make similar methodological arguments whether they utilise frequentist or Bayesian approaches to statistical inference. Indeed, Bayesian approaches to study design and statistical inference play a significant (and increasing) role in many sciences, often alongside frequentist approaches (clinical drug development provides a prominent example). It would have been interesting to see what, if any, common ground could be reached on these approaches to the philosophy of science (even if very little common ground seems possible in terms of their competing approach to statistical inference).