%0 Journal Article %A S.R.K. Zajitschek %A F. Zajitschek %A R. Bonduriansky %A R.C. Brooks %A W. Cornwell %A D.S. Falster %A M. Lagisz %A J. Mason %A A. M. Senior %A D. A. W. Noble %A S. Nakagawa %T Sex and Power: sexual dimorphism in trait variability and its eco-evolutionary and statistical implications %D 2020 %R 10.1101/2020.05.23.106146 %J bioRxiv %P 2020.05.23.106146 %X Biomedical and clinical sciences are experiencing a renewed interest in the fact that males and females differ in many anatomic, physiological, and behavioral traits. Sex differences in trait variability, however, are yet to receive similar recognition. In medical science, mammalian females are assumed to have higher trait variability due to estrus cycles (the ‘estrus-mediated variability hypothesis’); historically in biomedical research, females have been excluded for this reason. Contrastingly, evolutionary theory and associated data support the ‘greater male variability hypothesis’. Here, we test these competing hypotheses in 218 traits measured in >27,000 mice, using meta-analysis methods. Neither hypothesis could universally explain patterns in trait variability. Sex-bias in variability was trait-dependent. While greater male variability was found in morphological traits, females were much more variable in immunological traits. Sex-specific variability has eco-evolutionary ramifications including sex-dependent responses to climate change, as well as statistical implications including power analysis considering sex difference in variance.Significance Statement Males and females differ in many traits. However, we know relatively little about sex differences in trait variability. In many clinical contexts, female subjects have traditionally been excluded, due to assumed higher variability caused by the estrus cycle. Contrastingly, theory from evolutionary biology predicts higher variability in males. Neither explanation universally fits the data, but specific trait groups exhibit strong sex-specific differences. Sex differences in trait variability implies, for example, that the two sexes respond differently to environmental changes, and one sex could fair worse than the other depending on the nature of changes. Also, such sex differences mean that we should regularly include both males and females in biomedical trials, carrying out statistical power calculations separately for both sexes.Competing Interest StatementThe authors have declared no competing interest. %U https://www.biorxiv.org/content/biorxiv/early/2020/05/26/2020.05.23.106146.full.pdf