Some weeks ago I discovered an error* in the upper severity bounds for the one-sided Normal test in section 5 of: “Statistical Science Meets Philosophy of Science Part 2″ SS & POS 2. The published article has been corrected. The error was in section 5.3, but I am blogging all of 5.
(* μo was written where xo should have been!)
5. The Error-Statistical Philosophy
I recommend moving away, once and for all, from the idea that frequentists must ‘sign up’ for either Neyman and Pearson, or Fisherian paradigms. As a philosopher of statistics I am prepared to admit to supplying the tools with an interpretation and an associated philosophy of inference. I am not concerned to prove this is what any of the founders ‘really meant’.
Fisherian simple-significance tests, with their single null hypothesis and at most an idea of a directional alternative (and a corresponding notion of the ‘sensitivity’ of a test), are commonly distinguished from Neyman and Pearson tests, where the null and alternative exhaust the parameter space, and the corresponding notion of power is explicit. On the interpretation of tests that I am proposing, these are just two of the various types of testing contexts appropriate for different questions of interest. My use of a distinct term, ‘error statistics’, frees us from the bogeymen and bogeywomen often associated with ‘classical’ statistics, and it is to be hoped that that term is shelved. (Even ‘sampling theory’, technically correct, does not seem to represent the key point: the sampling distribution matters in order to evaluate error probabilities, and thereby assess corroboration or severity associated with claims of interest.) Nor do I see that my comments turn on whether one replaces frequencies with ‘propensities’ (whatever they are). Read more
