> Bayesian statistics doesn’t give a satisfying answer/method for choosing priors I absolutely agree with you. Priors are the (very difficult) job of the pract

> Bayesian statistics doesn’t give a satisfying answer/method for choosing priors I absolutely agree with you. Priors are the (very difficult) job of the practitioner. > choosing priors is a part of statistics Ah so I think I agree, maybe would nitpick and say priors are part of statistics and CHOOSING priors is part of modeling maybe? Not totally comfortable with that characterization either it’s just that “priors” and “choosing priors” are two different things. > therefore Bayesian statistics has a “hole” in it. Completely disagree; this is my main understanding of the argument against Bayesian statistics, I just don’t understand how this follows from the last statement. Priors are hard. But they are explicit! And a necessary ingredient of the posterior. It’s not like we can say “oh priors are hard, so let’s not do Bayesian stats but instead let’s do X” what is X?