I guess everyone here already understands this stuff, but I'll still try to summarize why "model checking" is an argument against "naive Bayesians" like Eliezer's OB persona. Shalizi has written about this at length on his blog and elsewhere, as has Gelman, but maybe I can make the argument a little clearer for novices.

Imagine you have a prior, then some data comes in, you update and obtain a posterior that overwhelmingly supports one hypothesis. The Bayesian is supposed to say "done" at this point. But we're actually not done. We have only "used all the information available in the sample" in the Bayesian sense, but not in the colloquial sense!

See, after locating the hypothesis, we can run some simple statistical checks on the hypothesis and the data to see if our prior was wrong. For example, plot the data as a histogram, and plot the hypothesis as another histogram, and if there's a lot of data and the two histograms are wildly different, we know almost for certain that the prior was wrong. As a responsible scientist, I'd do this kind of check. The catch is, a perfect Bayesian wouldn't. The question is, why?