Equivalence Tests

TLDR

Researchers need reliable methods to support the absence of a meaningful effect, yet nonsignificant results are often misinterpreted; frequentist equivalence testing is a widely recommended solution. The article presents a two‑one‑sided‑tests (TOST) framework, with user‑friendly spreadsheet and R package tools, that lets researchers set standardized effect‑size bounds to statistically reject effects deemed too large to matter. Using equivalence tests enhances both statistical rigor and theoretical inference.

Abstract

Scientists should be able to provide support for the absence of a meaningful effect. Currently, researchers often incorrectly conclude an effect is absent based a nonsignificant result. A widely recommended approach within a frequentist framework is to test for equivalence. In equivalence tests, such as the two one-sided tests (TOST) procedure discussed in this article, an upper and lower equivalence bound is specified based on the smallest effect size of interest. The TOST procedure can be used to statistically reject the presence of effects large enough to be considered worthwhile. This practical primer with accompanying spreadsheet and R package enables psychologists to easily perform equivalence tests (and power analyses) by setting equivalence bounds based on standardized effect sizes and provides recommendations to prespecify equivalence bounds. Extending your statistical tool kit with equivalence tests is an easy way to improve your statistical and theoretical inferences.