  Bayesian hypothesis testing is re-examined from the perspective of an a
priori assessment of the test statistic distribution under the alternative. By
assessing the distribution of an observable test statistic, rather than prior
parameter values, we provide a practical default Bayes factor which is
straightforward to interpret. To illustrate our methodology, we provide
examples where evidence for a Bayesian strikingly supports the null, but leads
to rejection under a classical test. Finally, we conclude with directions for
future research.
