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Equivalence test

Equivalence tests are a variety of hypothesis tests used to draw statistical inferences from observed data. In these tests, the null hypothesis is defined as an effect large enough to be deemed interesting, specified by an equivalence bound. The alternative hypothesis is any effect that is less extreme than said equivalence bound. The observed data are statistically compared against the equivalence bounds. If the statistical test indicates the observed data is surprising, assuming that true effects are at least as extreme as the equivalence bounds, a Neyman-Pearson approach to statistical inferences can be used to reject effect sizes larger than the equivalence bounds with a pre-specified Type 1 error rate.

TOST procedure
A very simple equivalence testing approach is the ‘two one-sided t-tests’ (TOST) procedure. In the TOST procedure an upper (ΔU) and lower (–ΔL) equivalence bound is specified based on the smallest effect size of interest (e.g., a positive or negative difference of d = 0.3). Two composite null hypotheses are tested: H01: Δ ≤ –ΔL and H02: Δ ≥ ΔU. When both these one-sided tests can be statistically rejected, we can conclude that –ΔL U, or that the observed effect falls within the equivalence bounds and is statistically smaller than any effect deemed worthwhile and considered practically equivalent. A recent modification to TOST makes the approach feasible in cases of repeated measures and assessing multiple variables. == Comparison between t-test and equivalence test ==
Comparison between t-test and equivalence test
The equivalence test can be induced from the t-test. == See also ==
Literature
The papers below are good introductions to equivalence testing. • • • • • • • • An applied introduction to equivalence testing appears in Section 4.2 of Vincent Arel-Bundock’s open-access book Model to Meaning. == References ==
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