Replying since it's been too long to edit: Now that I've taken some time to actually READ the article referenced in this post, I have a couple more things to s

Replying since it's been too long to edit: Now that I've taken some time to actually READ the article referenced in this post, I have a couple more things to say; basically: I agree with virtually everything that they're saying in the paper (except that I still don't understand why Bayesian stats failing in QM says absolutely anything about the validity of Bayesian stats), and I think I set up my own straw man here a bit. I thought this was another "Bayesian stats is bad because priors" post, which in a way it SORT of is, but their arguments are a bit more on the side of "you should take 'Bayesian' analyses with a large grain of salt sometimes because...well...priors" <- and THAT I completely agree with. The only beef I have with these types of articles are when they seem to imply that because priors sometimes have an outsized effect on the results, we should not use Bayesian statistics/analyses. But really, there is no other option. If you have a statistical question, you're stuck with having to answer for your priors. That being said, what IS good advice is to not simply believe every 'Bayesian' conclusion for exactly that reason -- that result is only as good as the (1) prior, (2) model assumptions, and (3) the data. (2) and (3) are easy to fight about, but (1) can be hard to evaluate, and easily can lead all of us astray even when the prior sounds reasonable (flat priors).