Fig. 1: Hazards of pooling data from both sexes.

Pooling data across sexes not only assumes that there is no difference between males and females, but also subsequently prevents researchers from testing for the dependency of an experimental response on the sex of a study participant. a, The theoretical examples reveal that pooling (green circles) masks important male (orange triangles) and female (blue squares) differences in baseline data, treatment response and sex × treatment interactions—any one of which leads to misinterpretation of the results. b, An example of experimental data in which pooling would have masked both the sex difference in the respiration rate of copepods, as well as the response of this variable to increased levels of \({p}_{{{\rm{CO}}}_{2}}\). Theoretical examples were generated using hypothetical data; experimental data were taken from a previously published study¹².