Frequentist vs Bayesian

Frequentists treat parameters that define distributions as fixed, and the randomness comes from the sampling data itself, so they construct estimators, confidence intervals, and hypothesis tests to make inferences about those fixed parameters.

Bayesians treat the parameters as random and assign prior distributions to them, which in light of data are updated through Bayes’ theorem, yielding a posterior distribution that directly quantifies uncertainty about the parameters.