Discussion questions#
In Monte Carlo power analysis, you define the “true” effect size and noise level. How confident should you be in results that depend entirely on assumptions you chose? If two researchers run Monte Carlo power analyses with different assumptions and get different required sample sizes, who is “right”?
If Monte Carlo methods are more flexible and realistic, why would anyone still use traditional (analytic) power analysis (e.g., G power)?