An equivalence test is a test that allows to conclude, with a specified confidence level, equivalence between observations.
When using equivalence tests, you must specify how large of a difference between group averages would represent a clinically or practically important (significant) difference, or how large a difference can be to be still considered insignificant. These limits must be decided beforehand and are based on scientific and clinical judgment, or industry-specific standards.
Equivalence may be concluded when the observed differences are smaller than the pre-specified maximum insignificant differences. When differences are larger than the pre-specified limits, equivalence is rejected.
In MedCalc, Schuirmann’s (1987) "two one-sided tests" (TOST) approach can be used to test equivalence. To do this, you
- define the range of differences that are not clinically or practically important (i.e. not significant)
- depending on the experimental design, use the Independent samples t-test or Paired samples t-test
- select the option for a 90% (!) Confidence Interval
- if the entire range of the 90% Confidence Interval of the mean difference lies within prespecified range of indifference, then conclude with 95% confidence that the two treatments are equivalent.
- Schuirmann DJ (1987) A comparison of the Two One-Sided Tests Procedure and the Power Approach for assessing the equivalence of average bioavailability. Journal of Pharmacokinetics and Biopharmaceutics 15:657-680.
- Equivalence test on Wikipedia