I've been learning statistics for a long time but I still struggle to understand the "philosophical" differences between frequentist and bayesian statistics.

AFAIK, frequentist and bayesian are two interpretations of the concept of probability. Frequentists define it as the long-run frequency of an event after many trials. Bayesians define it as our degree of expectation that the event will happen. But these definitions leave out a few gaps that I hope you can help me fill:

Under the bayesian interpretation, do we still believe that these events follow a "population" probability distribution with defined but unknown parameters (mean, variance, etc). In other words, if we want to find out the average height in a village, and we update our prior with every measurement, are we still trying to zero in on the "population" height distribution?

P.S.: This question was associated with a different one before, but the other question does not answer my question.

  • $\begingroup$ See stats.stackexchange.com/questions/173056/… $\endgroup$
    – Tim
    Jul 21, 2022 at 16:31
  • $\begingroup$ Hi Tim, I read that question and they don't answer mine there. Where do you see that they did? $\endgroup$
    – Paca
    Jul 21, 2022 at 17:02