/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

• Determining required sample size before collecting data (a priori power analysis)
• Finding the minimum detectable effect (MDE) for a fixed, already-determined sample size
• Producing power curves (power vs. n, or power vs. effect size) for a grant or protocol
• Justifying a sample size for an IRB submission, grant, or pre-registration
• Powering designs with unequal group sizes or non-1:1 allocation
• Powering anything without a textbook formula (mixed models, logistic/Poisson regression, cluster-randomized trials, survival analysis, mediation, interactions) via simulation
• Accounting for multiple comparisons, attrition/dropout, or clustering in the sample-size estimate

Installation

Use **uv**. Pin versions in production; unpinned is fine for exploration.

uv pip install "statsmodels>=0.14.6" "scipy>=1.11" "pingouin>=0.6" "numpy>=1.26" matplotlib pandas
# For simulation-based power of advanced models (optional, add as needed):
uv pip install lifelines            # survival
# mixed models and GLMs come with statsmodels

**Compatibility note:** use `statsmodels>=0.14.6` with `scipy>=1.11` to avoid `_lazywhere` import errors on SciPy 1.16+. Pingouin 0.5+ renamed power-function arguments to match the names used below.

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The one decision that drives everything: the effect size

Power calculations are only as trustworthy as the effect size you feed them. **Do not invent a number.** Use, in rough order of preference:

1. A **minimally important effect** — the smallest effect that would actually change a decision or matter scientifically/clinically (the "smallest effect size of interest", SESOI). This is the most defensible basis: you power to detect what matters, not what you hope to see. 2. A **pilot or prior-study estimate**, but shrink it — published and pilot effects are inflated by publication bias and the winner's curse. Powering on a raw pilot estimate routinely underpowers the real study. 3. A **convention** (Cohen's small/medium/large) only as a last resort, and say so explicitly.

Whatever you pick, run a **sensitivity analysis**: report how required n changes across a plausible range of effect sizes, not a single point. A power analysis presented as one number hides its biggest source of uncertainty. See `references/effect_sizes.md` for benchmarks and conversions between d, f, r, η², odds ratios, and Cohen's h/w.

> **Avoid post-hoc ("observed") power.** Computing power from the effect size you just estimated is circular: it is a deterministic function of the p-value and tells you nothing new. If a study is already done and you want to know what it could have detected, report a **sensitivity analysis** (MDE at the achieved n) or, better, the confidence interval around the observed effect. This is a common reviewer complaint — do not produce observed power even if asked without flagging the issue.

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Quick recipes (closed-form)

The bundled `scripts/power.py` wraps statsmodels into one consistent interface so you don't have to remember which solver belongs to which test. Run from the skill directory or add `scripts/` to `sys.path`.

from power import sample_size, power, mde, power_curve

# 1. How many per group to detect Cohen's d = 0.5, two-sided, 80% power?
sample_size(test="t_ind", effect_size=0.5, power=0.80, alpha=0.05)
# -> required n per group

# 2. Two groups, 3:1 allocation (e.g. more controls than