Yeah. Further context, for other readers: it's important to distinguish between Bayesianism the intellectual movement and Bayesian modeling as it exists in the

Yeah. Further context, for other readers: it's important to distinguish between Bayesianism the intellectual movement and Bayesian modeling as it exists in the professional and academic statistical community. There are communities which pick up Bayes Theorem in isolation (i.e. without any other statistical education or knowledge) and apply it to every debate and discussion under the Sun, often pontificating about how rational they're being about "updating their priors." But using Bayes theorem qualitatively instead of quantitatively - and without regard for its pitfalls - often leads to confirmation bias in which someone lends a veneer of rigorous probability to something they already unconsciously believed anyway. It's also sometimes used to dress up pseudoscience with intellectual polish, and it takes work to unpack those errors. Bayesian probability is not a panacea, and does not displace frequentist probability. The two paradigms are fully compatible (you can move from one to the other via inversion of the estimator function). But they answer different questions, both of which are important. It is also easy to make statistical errors in both paradigms.