  Equivalence testing allows one to conclude that two characteristics are
practically equivalent. We propose a framework for fast sample size
determination with Bayesian equivalence tests facilitated via posterior
probabilities. We assume that data are generated using statistical models with
fixed parameters for the purposes of sample size determination. Our framework
defines a distribution for the sample size that controls the length of
posterior highest density intervals, where targets for the interval length are
calibrated to yield desired power for the equivalence test. We prove the
normality of the limiting distribution for the sample size and introduce a
two-stage approach for estimating this distribution in the nonlimiting case.
This approach is much faster than traditional power calculations for Bayesian
equivalence tests, and it requires users to make fewer choices than traditional
simulation-based methods for Bayesian sample size determination.
