Statistical simulations show that scientists need not increase overall sample size by default when including both sexes in in vivo studies
Fig 6
The impact of an opposite treatment effect on statistical power.
Results of simulations to calculate statistical power where there is an interaction driven by opposite treatment effect in each sex. (A) Illustrative plots of constructed datasets, ranging from no effect to maximum opposite effect in each sex (0–1 male, 0 to −1 female). (B) Statistical power to detect the main effect of treatment as a function of the interaction effect size for each 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.g006