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Equivalence testing for standardized effect sizes in linear regression

by   Harlan Campbell, et al.
The University of British Columbia

In this paper, we introduce equivalence testing procedures for standardized effect sizes in a linear regression. We show how to define valid hypotheses and calculate p-values for these tests. Such tests are necessary to confirm the lack of a meaningful association between an outcome and predictors. A simulation study is conducted to examine type I error rates and statistical power. We also compare using equivalence testing as part of a frequentist testing scheme with an alternative Bayesian testing approach. The results indicate that the proposed equivalence test is a potentially useful tool for ”testing the null.”


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