It boils down to whether you give precedence to the likelihood principle or the strong repeated sampling principle (Bayes prefers the likelihood principle and Frequentist prefers repeated sampling). See Cox and Hinkley's Theoretical Statistics for a full discussion, but basically the likelihood principle states that all conclusions should be based exclusively on the likelihood function; in layman's terms, on the data themselves. This specifically omits what a frequentist would call important contextual metadata, like whether the sample size is random, why the sample size is what it is, etc.
The strong repeated sampling principle states that the goodness of a statistical procedure should be evaluated based on performance under hypothetical repetitions. Bayesians often dismiss this as: "what are these hypothetical repetitions? Why should I care?"
Well, it depends. If you're predicting the results of an election, it's a special 1 time event. It isn't obvious what a repetition would mean. If you're analyzing an A/B test it's easy to imagine running another test, some other team running the same test, etc. Frequentist statistics values consistency here, more so than Bayesian methods do.
That's not to come out in support of one vs the other. You need to understand the strengths and drawbacks of each and decide situationally which to use. (Disclaimer: I consider myself a Frequentist but sometimes use Bayesian methods.)