Is "not different" enough to conclude similar cardiovascular responses across sexes?

The number of research studies investigating whether similar or different cardiovascular responses or adaptations exist between males and females is increasing. Traditionally, difference-based statistical methods, e.g., t test, ANOVA, etc., have been implemented to compare cardiovascular function between males and females, with a P value of >0.05 used to denote similarity between sexes. However, an absence of evidence, i.e., large P value, is not evidence of absence, i.e., no sex differences. Equivalence testing determines whether two measures or groups provide statistically equivalent outcomes, in that they differ by less than an "ideally prespecified" smallest effect size of interest. Our perspective discusses the applicability and utility of integrating equivalence testing when conducting sex comparisons in cardiovascular research. An emphasis is placed on how cardiovascular researchers may conduct equivalence testing across multiple study designs, e.g., cross-sectional comparisons, repeated-measures intervention, etc. The strengths and weaknesses of this statistical tool are discussed. Equivalence analyses are relatively simple to conduct, may be used in conjunction with traditional hypothesis testing to interpret findings, and permit the determination of statistically equivalent responses between sexes. We recommend that cardiovascular researchers consider implementing equivalence testing to better our understanding of similar and different cardiovascular processes between sexes.