Dear Reader: Tonight marks the 2-year anniversary of this blog; so I’m reblogging my very first posts from 9/3/11 here and here (from the rickety old blog site)*. (One was the “about”.) The current blog was included once again in the top 50 statistics blogs. Amazingly, I have received e-mails from different parts of the world describing experimental recipes for the special concoction we exiles favor! (Mine is here.) If you can fly over to the Elbar Room, please join us: I’m treating everyone to doubles of Elbar Grease! Thanks for reading and contributing! D. G. Mayo
(*The old blogspot is a big mix; it was before Rejected blogs. Yes, I still use this old typewriter [ii])
“Did you hear the one about the frequentist . . .
- “who claimed that observing “heads” on a biased coin that lands heads with probability .05 is evidence of a statistically significant improvement over the standard treatment of diabetes, on the grounds that such an event occurs with low probability (.05)?”
- “who defended the reliability of his radiation reading, despite using a broken radiometer, on the grounds that most of the time he uses one that works, so on average he’s pretty reliable?”
Such jests may work for an after-dinner laugh, but if it turns out that, despite being retreads of “straw-men” fallacies, they form the basis of why some reject frequentist methods, then they are not such a laughing matter. But surely the drubbing of frequentist methods could not be based on a collection of howlers, could it? I invite the curious reader to stay and find out.
If we are to take the criticisms seriously, and put to one side the possibility that they are deliberate distortions of frequentist statistical methods, we need to identify their sources. To this end I consider two interrelated areas around which to organize foundational issues in statistics: (1) the roles of probability in induction and inference, and (2) the nature and goals of statistical inference in science or learning. Frequentist sampling statistics, which I prefer to call “error statistics,” continues to be raked over the coals in the foundational literature, but with little scrutiny of the presuppositions about goals and methods, without which the criticisms lose all force.
First, there is the supposition that an adequate account must assign degrees of probability to hypotheses, an assumption often called probabilism. Second, there is the assumption that the main, if not the only, goal of error-statistical methods is to evaluate long-run error rates. Given the wide latitude with which some critics define “controlling long-run error,” it is not surprising to find them arguing that (i) error statisticians approve of silly methods, and/or (ii) rival (e.g., Bayesian) accounts also satisfy error statistical demands. Absent this sleight of hand, Bayesian celebrants would have to go straight to the finale of their entertainment hour: a rousing rendition of “There’s No Theorem Like Bayes’s Theorem.”
Never mind that frequentists have responded to these criticisms, they keep popping up (verbatim) in many Bayesian textbooks and articles on philosophical foundations. The difficulty of articulating a statistical philosophy that fully explains the basis for both (i) insisting on error-statistical guarantees, while (ii) avoiding pathological examples in practice, has turned many a frequentist away from venturing into foundational battlegrounds. Some even concede the distorted perspectives drawn from overly literal and radical expositions of what Fisher, Neyman, and Pearson “really thought”. Many others just find the “statistical wars” distasteful.
Here is where I view my contribution—as a philosopher of science—to the long-standing debate: not merely to call attention to the howlers that pass as legitimate criticisms of frequentist error statistics, but also to sketch the main lines of an alternative statistical philosophy within which to better articulate the roles and value of frequentist tools. Let me be clear that I do not consider this the only philosophical framework for frequentist statistics—different terminology could do as well. I will consider myself successful if I can provide one way of building, or one standpoint from which to build, a frequentist, error- statistical philosophy.
But given this is a blog, I shall be direct and to the point: I hope to cultivate the interests of others who might want to promote intellectual honesty within a generally very lopsided philosophical debate. I will begin with the first entry to the comedy routine, as it is put forth by leading Bayesians……
“Frequentists in Exile” 9/3/11 by D. Mayo
Confronted with the position that “arguments for this personalistic theory were so persuasive that anything to any extent inconsistent with that theory should be discarded” (Cox 2006, 196), frequentists might have seen themselves in a kind of exile when it came to foundations, even those who had been active in the dialogues of an earlier period [i]. Sometime around the late 1990s there were signs that this was changing. Regardless of the explanation, the fact that it did occur and is occurring is of central importance to statistical philosophy.
Now that Bayesians have stepped off their a priori pedestal, it may be hoped that a genuinely deep scrutiny of the frequentist and Bayesian accounts will occur. In some corners of practice it appears that frequentist error statistical foundations are being discovered anew. Perhaps frequentist foundations, never made fully explicit, but at most lying deep below the ocean floor, are finally being disinterred. But let’s learn from some of the mistakes in the earlier attempts to understand it. With this goal I invite you to join me in some deep water drilling, here as I cast about on my Isle of Elba.
Cox, D. R. (2006), Principles of Statistical Inference, CUP.
[i] Yes, that’s the Elba connection: Napolean’s exile (from which he returned to fight more battles).
[ii] I have discovered a very reliable antique typewriter shop in Oxford that was able to replace the two missing typewriter keys. So long as my “ribbons” and carbon sheets don’t run out, I’m set.