Equivalence tests from the null hypothesis significance testing framework are appropriate alternatives to difference tests for demonstrating lack of difference. For determining equivalence among more than two repeated measurements, recently developed equivalence tests include the omnibus Hotelling T2, the pairwise standardized test, the pairwise unstandardized test, and the two one-sided test for negligible trend. With Monte Carlo simulations, the current research evaluated Type I error rates and power rates for these equivalence tests to inform an applied data analytic strategy. Because results suggest that there is no one statistical test that is optimal across all situations, I compare the tests’ statistical behaviours to provide guidance in test selection. Specifically, test selection should be informed by the measurement level of the repeated outcome, correlation structure, and precision
