Statistical Power

---
name: statistical-power
description: Sample-size and statistical power calculations for planning studies. Use whenever 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 designs with no formula — logistic/Poisson regression, mixed models, cluster-randomized trials, survival, and interactions. Use this skill even when the request only mentions 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.
allowed-tools: Read Write Edit Bash
compatibility: Requires Python >=3.10. Examples target statsmodels >=0.14.6, scipy >=1.11, pingouin >=0.6, numpy >=1.26, and matplotlib. Optional extras are statsmodels mixed models and lifelines for simulation-based power.
license: MIT license
metadata: {"version": "1.0", "skill-author": "K-Dense Inc."}
---

# Statistical Power & Sample Size

## Overview

Power analysis answers one of the most consequential questions in study planning: **how large a sample do you need to reliably detect an effect of a given size, and what could you detect with the sample you can afford?** An underpowered study wastes resources and produces inconclusive or irreproducible results; an overpowered one wastes participants, money, and (in clinical work) exposes more people to risk than necessary. Getting this right *before* data collection is the single highest-leverage statistical decision in a project.

Four quantities are locked together for any given test: **sample size (n)**, **effect size**, **significance level (α)**, and **power (1 − β)**. Fix any three and the fourth is determined. Every calculation in this skill is some rearrangement of that relationship.

This skill covers the two ways to do power analysis:
- **Closed-form** formulas (fast, exact for standard tests) — see `references/closed_form_recipes.md`.
- **Simulation / Monte Carlo** (works for *any* design or model you can simulate and analyze) — see `references/simulation_based_power.md`.

For choosing and converting effect sizes — usually the hardest part — see `references/effect_sizes.md`.

## When to Use This Skill
… load the full skill through Skill Me