power-analysis

Calculate required sample size for a t-test by inputting your test type, effect size, alpha, and power into the interactive calculator, which outputs per-group and total sample sizes for a priori study planning.

Yes, you can justify sample size in a grant or protocol for an ANOVA design by running post-hoc or a priori power calculations that generate required sample sizes and power curves for protocol development.

Supported statistical tests for power and sample size planning include t-tests, ANOVA, chi-square, correlation, and regression, covering common experimental designs across numpy, scipy, and statsmodels.

Determine the right effect size for your sample size calculation using the calculator's built-in effect-size guidance, which helps translate pilot data or expected outcomes into appropriate inputs for sensitivity analyses.

Yes, this power analysis tool works for post-hoc sensitivity analyses by computing achieved statistical power based on observed effect sizes, alpha levels, and actual sample sizes from completed studies.

Plan pilot data studies using power curves by scripting the calculator to generate power across varying sample sizes and effect sizes, allowing you to optimize experimental design and assess feasibility before full data collection.