My impression is that philosophers and statisticians are often working with different focal examples. I think that in many fields important scientific knowledge essentially takes the form of a point estimate (e.g. the R0 of Covid is XXXX). It is also easy to come up with useful priors (e.g. the R0 is likely below 20) that arise more from characteristics of the model rather than theory.
Note that it is possible to reformulate the Covid example into a Null hypothesis test at the cost of being less informative (e.g. Is the R0 significantly above 1?) but then the knowledge becomes less useful for making certain important decisions.
Anyways, my general impression is that Bayesian statistics are probably more useful for making good decisions that require precise numerical knowledge of certain types of information but maybe less useful for many of the sorts of conceptual issues philosophers are often interested in.
Note that it is possible to reformulate the Covid example into a Null hypothesis test at the cost of being less informative (e.g. Is the R0 significantly above 1?) but then the knowledge becomes less useful for making certain important decisions.
Anyways, my general impression is that Bayesian statistics are probably more useful for making good decisions that require precise numerical knowledge of certain types of information but maybe less useful for many of the sorts of conceptual issues philosophers are often interested in.