Generally, the more data you have, the less your prior influences the results. And the less your prior influences the results, the more frequentist and Bayesian

Generally, the more data you have, the less your prior influences the results. And the less your prior influences the results, the more frequentist and Bayesian analysis will "agree" on the outcome. Note that there are still some epistemological differences that will cause the two camps to speak slightly different languages. Bayesians will have (a very peaky) posterior distribution on the parameters they care about, and frequentists will have (a very tight) confidence interval around the same parameter. For problems where you have small amounts of data but a lot of intuition about how the where the data comes from, then Bayesian methods will typically give you a better answer. However, if you have a small amount of data and a bad prior, then your conclusions will be flawed. Essentially, it's garbage in, garbage out.