  We describe Bayes factors based on z, t, $\chi^2$, and F statistics when
non-local moment prior distributions are used to define alternative hypotheses.
The non-local alternative prior distributions are centered on standardized
effects. The prior densities include a dispersion parameter that can be used to
model prior precision and the variation of effect sizes across replicated
experiments. We examine the convergence rates of Bayes factors under true null
and true alternative hypotheses and show how these Bayes factors can be used to
construct Bayes factor functions. An example illustrates the application of
resulting Bayes factors to psychological experiments.
