I really never understood the divide. They are all just tools in our toolbox. It feels like arguing screwdrivers versus hammers. They may not do the same thing, but both can be useful!
One downside of this ecumenicalism is a reluctance to ask fundamental questions: having a strong opinion on this matter has gone out of fashion. Who’s to say one statistical philosophy is better than another? Aren’t all statistical philosophies equally valid paths to good data analysis? Frequentism is “true for me”. As in religion, so in statistics. If you criticise a colleague for using p-values when posterior probabilities are clearly more appropriate, it will lead to accusations of being a ‘zealot’ [2] who should stop ‘crusading’
and then later:
If Einstein’s theory were easier (as well as being more accurate), teaching Newton’s would be silly. Yet that’s the way most statistics curricula are structured. The only reason statisticians think frequentist ideas are easier is that they are used to them
The point about teaching relativistic versus newtonian mechanics is totally different because they're incompatible. One is more correct than the other, which is not the case here. In the stats case, both tools give correct answers to different questions.
Maybe I just get frustrated feeling like people want a data analysis silver bullet to teach.
Edit: one can argue that frequentist methods are generally approximations to Bayesian methods, much like Newton vs Einstein
There are some consistency properties of Bayesianism which make it a much better candidate for epistemology than frequentism. Specifically, there are important questions in frequentism for which there are no answers. In particular, the concept of "power of a test" sweeps the prior under the rug without a proper explanation.
Now if you're tempted to use one or the other in a case by case basis, how are we supposed to choose which is best?