Individual skill
statistical-power
Calculates sample sizes and statistical power for study planning. Applies when someone asks "how many subjects/samples/replicates do I need", wants an a priori power analysis, a minimum detectable effect (MDE), a power curve, or needs to justify a sample size for a grant, IRB protocol, or pre-registration. Covers closed-form power for t-tests, ANOVA, proportions, correlations, chi-square, and regression, plus simulation-based (Monte Carlo) power for complex designs — logistic/Poisson regression, mixed models, cluster-randomized trials, survival, and interactions. Also handles requests that only mention an effect size, alpha, or "80% power" without saying "power analysis" explicitly. For laying out the study (randomization, blocking, factorial/DOE, crossover, sequential designs) use experimental-design; for analyzing data already collected and reporting it use statistical-analysis.
skills/statistical-power/SKILL.md
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- Last checked
- Nov 10, 2026
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- Repository last updated
- Nov 9, 2026
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- MIT
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- ai-scientist Repository topic
- bioinformatics Repository topic
- chemoinformatics Repository topic
- claude Repository topic
- claude-skills Repository topic
- claudecode Repository topic
- clinical-research Repository topic
- computational-biology Repository topic
- data-analysis Repository topic
- drug-discovery Repository topic
- design Derived category
- data Derived category
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SKILL.mdcore · 17,834 bytesSKILL.mdcore · 17,834 bytesSKILL.mdcore · 17,834 bytesSKILL.mdcore · 17,834 bytesSKILL.mdcore · 17,834 bytesSKILL.mdcore · 17,834 bytesSKILL.mdcore · 17,834 bytesSKILL.mdcore · 17,834 bytesSKILL.mdcore · 17,834 bytesreferences/closed_form_recipes.mddoc · 11,159 bytesreferences/effect_sizes.mddoc · 6,110 bytesreferences/simulation_based_power.mddoc · 10,163 bytesscripts/power.pyruns · 19,966 bytesscripts/simulate_power.pyruns · 13,193 bytes
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# Statistical Power & Sample Size
## Overview
Power analysis plans the probability of rejecting a specified null under an assumed alternative. It answers **how many independent experimental units are needed to detect a scientifically important effect**, or what effects a feasible sample could detect. Choose the inferential goal first: precision, equivalence, noninferiority, or sequential monitoring need their own calculations; the bundled superiority-test helpers do not cover them.
Four quantities are locked together for any given test: **sample size (n)**, **effect size**, **significance level (α)**, and **power (1 − β)**. For a fixed design, analysis, and nuisance parameters, fixing three permits solving for the fourth when a solution exists. Every calculation in this skill is some rearrangement of that relationship.
This skill covers the two ways to do power analysis:
- **Analytical** power calculations (exact under some standard-test assumptions; asymptotic approximations for others) — see `references/closed_form_recipes.md`.
- **Simulation / Monte Carlo** (requires a credible data-generating process and a calibrated planned analysis) — see `references/simulation_based_power.md`.
For choosing and converting effect sizes — usually the hardest part — see `references/effect_sizes.md`.
