Based on Nyan's subsequent post, what he was trying to say was: "get more data," which is a point, as he correctly points out (not in the OP though) that is orthogonal to B vs F.

Ok. I guess I was confused by this start:

"Bayesian epistemology and decision theory provide a rigorous foundation for dealing with mixed or ambiguous evidence, uncertainty, and risky decisions. You can't always get the epistemic conditions that classical techniques like logic or maximum liklihood require, so this is seriously valuable."

Also by the fact that the word "Bayesian" is used lots of times in the OP. I like causal graphs. It doesn't mean I have to sprinkle them into every post I make on every subject :).

I was not trying to cherry pick examples for any particular purpose. These examples were all difficult decisions I had to make in my life, except the last one, which is an academic example where B vs F considerations are very subtle. I don't know what it means to "go Bayesian" on these examples. What made them difficult was not the kind of thing that Bayes theorem would have made easier.

I guess my view is, unless you are doing stats/machine learning (and maybe not even then!), you ought to have no opinion on B vs F. This is an argument that will not affect your life.