Improving Inferences About Null Effects With Bayes Factors and Equivalence Tests.Researchers often conclude an effect is absent when a null-hypothesis significance test yields a nonsignificant p value.  We present two methods to evaluate the presence or absence of effects: Equivalence testing (based on frequentist statistics) and Bayes factors (based on Bayesian statistics).  We provide detailed explanations of how to calculate, report, and interpret Bayes factors and equivalence tests.  The conceptual differences between Bayes factors and equivalence tests are discussed, and we also note when and why they might lead to similar or different inferences in practice.  Bayes factors and equivalence tests provide useful statistical tools to improve inferences about null effects.
Bayes Factors
Improving Inferences About Null Effects With Bayes Factors and Equivalence Tests.Researchers often conclude an effect is absent when a null-hypothesis significance test yields a nonsignificant p value.  We provide detailed explanations of how to calculate, report, and interpret Bayes factors and equivalence tests.  The conceptual differences between Bayes factors and equivalence tests are discussed, and we also note when and why they might lead to similar or different inferences in practice.  Bayes factors and equivalence tests provide useful statistical tools to improve inferences about null effects.
Equivalence Tests