Possible idea for a post:
There isn't much material here on the problem of multiple comparisons. This is something that humans routinely stumble over, while for an ideal Bayesian it wouldn't even be a problem requiring a solution (much like e.g. confirmation bias). The post would describe the multiple comparisons problem, explain why it's a non-issue for Bayesians, and look into plausible candidates for the psychological mechanisms that give rise to it (hindsight bias, privileging the hypothesis, base-rate neglect; any others?).
Reply here if you are (actually) starting to work on this.
I'd love to see a post on this, ideally with R code. In particular, I need to know about this because I'm running a big sleep experiment with 5 separate interventions, each with multiple endpoints. You can see the problem.
I've done multiple correction of p-values with my previous frequentist analyses with the same problem of multiple endpoints, but I'd rather do a Bayesian analysis; however, I don't know how to do multiple correction with Bayesian results. Reading, a Gelman paper tells me that I don't need to because if I'm doing hierarchical models, prob...
If it's worth saying, but not worth its own post, even in Discussion, it goes here.