Should we worry about rigged priors? A long discussion.

Today's discussion starts with Stuart Buck, who came across a post by John Cook linking to my post, "Bayesian statistics: What's it all about?". Cook wrote about the benefit of prior distributions in making assumptions explicit. Buck shared Cook's post with Jon Baron, who wrote: My concern is that if researchers are systematically too optimistic (or even self-deluded) about about the prior evidence--which I think is usually the case--then using prior distributions as the basis for their new study can lead to too much statistical confidence in the study's results. And so could compound the problem. My response to Jon is that I think all aspects of a model should be justified.