The need for theory

hmmm, that doesn't rhyme quite as well. Ben Bolker brought the following quote from Efron and Tibshirani (1986; "Bootstrap methods for standard errors ...") to my attention:

An important theme of what follows is the substitution of computing power for theoretical analysis. This is not an argument against theory, of course, only against unnecessary theory.

I've often thought of the need for theory as falling along a continuum of 1/n, so when your sample size is small you need strong theory to make predictions, and when large you can get away with less theory. In either case it helps if your theory is well tested in other cases, or you risk making predictions that are completely bogus.