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.