Statistical simulations show that scientists need not increase overall sample size by default when including both sexes in in vivo studies
Fig 4
The impact of a sex-dependent treatment effect on statistical power.
Results of simulations to calculate statistical power where there is an interaction between treatment and sex in the data where the interaction effect is in the same direction as the main treatment effect. (A) Illustrative plots of constructed datasets, ranging from no effect to maximum treatment by sex interaction (none = 0, small = 0.5, large = 1) for varying sizes of a main treatment effect (0–1). (B) Statistical power to detect treatment effect per size of interaction effect and statistical method. (C) Power for each model term within factorial analysis output. (D) Power for post hoc comparison control vs. treated within each sex. Simulation N = 1,000 for each scenario assessed. For the graphs of power (B–D), the horizontal dashed line indicates the target power. The data underlying this figure can be found in https://doi.org/10.5281/zenodo.7806724.
doi: https://doi.org/10.1371/journal.pbio.3002129.g004