power-analysis

You can calculate required sample size for a two-arm comparison by running a Monte-Carlo simulation that estimates statistical power based on your baseline mean, standard deviation, and minimum detectable effect size.

Automating preregistration for an A/B test or randomized controlled trial involves generating a study design document that formally specifies your hypothesis, analysis plan, and validity checks before data collection begins.

To run a power analysis for a two-group behavioral study, you need your baseline mean, standard deviation, minimum effect size, target power (e.g. 0.80), alpha level (e.g. 0.05), and a specified study hypothesis.

Yes, you can use Python 3 to compute statistical power for study design by executing simulation scripts that automate the process, requiring Python 3.9 or higher and specific simulation tools.

The best way to ensure an RCT is appropriately powered is to conduct a power analysis using Monte-Carlo simulation to verify the sample size meets your target power, then preregister the design.

You should not use Monte-Carlo simulation for statistical power analysis if your study design falls outside simple two-arm comparisons, as this Skill specifically targets two-group studies and predefined validity checks.