[BeetleB]

Your criticism of p-value usage is legitimate. However, this is not core to frequentist statistics.

When I first encountered p-values, even with a frequentist mindset, I saw the huge problem that one could have with them. Many frequentists do not like p-values. I wouldn't be surprised if most actual frequentist statisticians (not those in fields like medicine, psychology, etc) do not like p-value usage.

Attacking p-values is not a valid argument against frequentist statistics.

I'll also add that it seems that many Bayesians are really dying for a number, and because frequentist stats doesn't give it to them, they reach for another tool that will - but with little thought about the validity of the tool. I'm not here to defend frequentist statistics, but just because it doesn't give all the answers, that does not mean that some other tool that does give some answers is correct.

It is equally abusable as p-values. I suppose if a Bayesian says he used Bayesian approaches because it made sense given his problem, that's fine (and in my mind, he is just being a statistician, not a Bayesian). The self-identified Bayesians I always encounter don't fall into that mold. They fall into the category of "Look what I can compute that I could not with frequentist statistics" - but any attempts I have to understand what that number means fails - they cannot explain it either, beyond "this is how I feel".

[eli_gottlieb]

I'm not really trying to make an argument against frequentist statistics and for Bayesian ones. I'm more trying to point out what each style exposes (by printing it in your papers) or conceals (by leaving it semi-consciously understood from that one class in grad school).