The Bactra Review   Error and the Growth of Experimental Knowledge
Yet more formally: The severity of passing S(T, H, e, d) = P(d(T(~H), H) > d(e, H)), where H is our hypothesis, e is our evidence, d( , ) indicates the discrepency between H and e, and T() stands for the testing procedure, considered as a random function from the situation (H is true, or it's false, ~H) to evidence. (I offer this as a gloss on the basis of "severity criterion 2a" on p. 180, for passing results: "There is a very high probability that test procedure T would yield a worse fit [than it does with e], if H were false.")

This is related to, but not the same as, the "power" of the Neyman-Pearson theory of statistical tests, i.e., the probability of not failing to reject H if it is false. In a fit of stupidity, I had identified the two, and thank Prof. Mayo not only for setting me straight, but for not rubbing it in as much as she justifiably could have.