[is74]

The problems with statistics is that it's complicated, both bayesian and frequentist. Specifically, all statistical methods make assumptions about the data, some of which are quite subtle and take effort to understand. Their intricacy is the reason why so many scientists use them incorrectly. It's much less about whether a method is bayesian or frequentist, but whether the specific assumptions made by a method are suitable for the data. This requires a judgement call. One of the advantages of Bayesian methods over frequentist methods is that it's easier to incorporate what we know about the data into a bayesian model using the bayesian prior straightforwardly, but only in principle, because in practice doing a good job is pretty tricky.