I hear you, and my understanding is that this largely depends on the discipline. In fields like medicine and psychology, the flourishing of classical methods i

I hear you, and my understanding is that this largely depends on the discipline. In fields like medicine and psychology, the flourishing of classical methods in the 30s and 40s and 50s did lead to a sort of dogmatism about p-values and a disdain for the old "inverse probability" (what we now call Bayesian methods) of the 18th and 19th centuries. These fields seem to be still recovering from this. My impression is that that's where you often find self-styled Bayesian rebels with the faith of the converted. In areas like radar or image processing or communications or information theory or ad placement or AI in general, I think the field has long had a more nuanced understanding of the underlying decision-theoretic concerns. When you have to win World War II and there is a cost to falsely identifying a Nazi aircraft as Allied, and a cost to falsely identifying an Allied aircraft as Nazi, you develop a notion of an ROC curve pretty quickly. Ditto when you want to talk to a Voyager probe and you're not sure if it just sent a 0 or a 1. In my view, the Bayesian vs. frequentist "debate" has little to say to these fields. Although for a contrary view, see what Jaynes writes about Shannon in "Probability Theory: The Logic of Science" in the last chapter ("Introduction to communication theory").