statistical-power
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
npx skills add K-Dense-AI/scientific-agent-skills --skill statistical-power --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# 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 h
- Overview
- When to Use This Skill
- Installation
- The one decision that drives everything: the effect size
- Quick recipes (closed-form)
- When there is no formula: simulate
- Adjustments people forget
- Workflow
- Reporting template
- Common pitfalls
- Resources
- Scripts
- References
- Related skills
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
What does the statistical-power skill do?
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
How do I install it?
Run `npx skills add K-Dense-AI/scientific-agent-skills --skill statistical-power --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From K-Dense-AI/scientific-agent-skills, a repository with 32,619 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.