  We point out that the Neyman-Pearson lemma applies to Bayes factors if we
consider expected type-1 and type-2 error rates. That is, the Bayes factor is
the test statistic that maximises the expected power for a fixed expected
type-1 error rate. For Bayes factors involving a simple null hypothesis, the
expected type-1 error rate is just the completely frequentist type-1 error
rate. Lastly we remark on connections between the Karlin-Rubin theorem and
uniformly most powerful tests, and Bayes factors. This provides frequentist
motivations for computing the Bayes factor and could help reconcile Bayesians
and frequentists.
