Sex in experiments: Design considerations, statistical analysis and interpretation

The inclusion of both sexes in preclinical research has raised practical questions about variability, statistical power, and required sample size. Much of this uncertainty arises from treating sex as a complication rather than as part of the experimental structure. Here I outline the statistical consequences of incorporating sex into experimental design and analysis. Adding sex converts a two-group comparison into a factorial experiment that simultaneously estimates treatment effects, baseline differences, and sex-dependent responses. This article outlines how experiments including both sexes can be designed, how main effects and interactions should be interpreted, how exploratory and confirmatory aims differ, and how visualization and reporting can prevent common errors. Importantly, powering studies for treatment effects generally requires little increase in animal numbers, whereas detecting interactions represent a distinct and inherently more demanding question.