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Statistical Power & Sample Size

Plans studies by computing required sample size, minimum detectable effect, and power curves via closed-form formulas or Monte Carlo simulation.

Data & AnalyticsIntermediate★ 33,030⑂ 3,248AI score 9/10Last updated: Aug 9, 2026

What it does

  • Runs a priori power analyses to answer "how many subjects/samples/replicates do I need?"
  • Handles standard tests (independent/paired t-tests, one-way ANOVA, one and two proportions, correlation, chi-square, regression R² increment) through one wrapper in scripts/power.py: sample_size, power, mde, power_curve.
  • For designs with no formula — logistic/Poisson regression, mixed models, cluster-randomized trials, survival, mediation, interactions — it uses scripts/simulate_power.py for Monte Carlo power with a reported simulation CI.
  • Enforces good practice: defensible effect sizes (SESOI > shrunk pilot > Cohen conventions), sensitivity analyses instead of a single n, dropout inflation, design effect 1 + (m−1)·ICC for clustering, and multiplicity-corrected alpha. Explicitly warns against post-hoc "observed" power.
  • Provides a ready-to-adapt reporting paragraph for grants, IRB protocols, and pre-registrations.

Who it's for

  • Researchers and grad students designing studies, theses, or clinical protocols.
  • Anyone who must justify a sample size to an IRB, ethics board, or funder.
  • Data analysts sizing experiments and A/B tests, or computing MDE for a fixed budget.
  • Reviewers/statisticians who want sensitivity analysis instead of circular observed power.

Example uses

  1. "How many per group to detect Cohen's d = 0.5 at 80% power, two-sided α = .05?" → n per group plus enrollment inflated for 20% attrition.
  2. "I can only recruit 30 per arm" → minimum detectable effect at that n, plus a power-curve figure for the proposal.
  3. "Cluster-randomized trial across 20 clinics with ICC = 0.05" → simulation search for the number of clusters and cluster size that reach target power.

· · · Install guide · · ·

Try it now, no install

Paste this into Claude to use the skill without installing anything.

Read the instructions in this file and follow them to help me:
https://raw.githubusercontent.com/K-Dense-AI/scientific-agent-skills/HEAD/skills/statistical-power/SKILL.md

What I want: (describe your task here)

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↓ If it works for you, download the ZIP below and install it. Then it runs on its own — no pasting each time.

Install in the Claude app (no terminal)
  1. Download the ZIP with the button below.
  2. In Claude, open Settings → Capabilities and turn on 'Code execution and file creation'. (one time)
  3. Go to Customize → Skills → + → 'Upload a skill' and upload the ZIP.
↓ Download ZIP
Install in Claude Code

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Install the skill I found on AI Skill Mart.
Copy the skills/statistical-power folder from the GitHub repo K-Dense-AI/scientific-agent-skills into my ~/.claude/skills/statistical-power/.
When it's done, tell me in one line what this skill can do.

Install with a command instead

git clone https://github.com/K-Dense-AI/scientific-agent-skills.git && mkdir -p ~/.claude/skills && cp -r scientific-agent-skills/skills/statistical-power ~/.claude/skills/

⚠ This is a third-party skill. Check the source repository before installing.

  1. Open a terminal and go to a working directory.
  2. Clone the repository: git clone https://github.com/K-Dense-AI/scientific-agent-skills.git
  3. Make sure the skills folder exists: mkdir -p ~/.claude/skills
  4. Copy just this skill: cp -r scientific-agent-skills/skills/statistical-power ~/.claude/skills/
  5. Verify Python 3.10 or newer: python3 --version
  6. Install dependencies: uv pip install "statsmodels>=0.14.6" "scipy>=1.11" "pingouin>=0.6" "numpy>=1.26" matplotlib pandas (add uv pip install lifelines for survival simulations).
  7. Restart Claude Code and ask something like "how many participants per group do I need for d = 0.5 at 80% power?" — the skill triggers automatically.
View source on GitHub ↗License: MIT