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statistical-power

Compute sample sizes, power estimates, and minimum detectable effects for study designs.

Updated Jul 1, 2026
Search Tags:#regression#monte carlo#sample size#power analysis#effect size#study planning
One-click install
npx skills add https://github.com/jasrajtulsi/GRAD-SCOPE --skill statistical-power-jasrajtulsi
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: statistical-power
Source: https://github.com/jasrajtulsi/GRAD-SCOPE/tree/main/.claude/skills/statistical-power
Command: npx skills add https://github.com/jasrajtulsi/GRAD-SCOPE --skill statistical-power-jasrajtulsi

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires numpy, statsmodels, scipy, matplotlib, pandas, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill helps you plan studies by determining how many observations you need, what effect size you can detect, and how much power your design has.

Core Features & Use Cases

  • Closed-form calculations for t-tests, ANOVA, proportions, correlation, chi-square, and regression.
  • Simulation-based power for logistic regression, mixed models, clustered trials, survival analysis, interactions, and other custom designs.
  • Use it to justify sample sizes for grants, IRB protocols, pre-registration, or to generate power curves and sensitivity analyses.

Quick Start

Ask for a power analysis for your planned study, specifying the test, effect size, target power, alpha, and allocation ratio if groups are unequal.

Frequently Asked Questions about statistical-power

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I calculate sample size for a study using power analysis?▼

Sample size calculation requires specifying your test type, effect size, target power, and alpha level. This Skill computes required observations for t-tests, ANOVA, proportions, correlation, chi-square, and regression using closed-form solvers.

Can I use Monte Carlo simulation for power analysis with mixed effects models?▼

Yes, Monte Carlo simulation estimates power for logistic regression, mixed models, clustered trials, and survival analysis. Simulation-based designs handle custom scenarios where closed-form calculations are not feasible.

What effect size do I need to detect a minimum detectable effect?▼

Minimum detectable effect calculations require effect-size conversion alongside your alpha and target power inputs. This Skill computes the smallest effect your study can reliably detect given your planned sample size and design.

How do I generate power curves and sensitivity analyses for grant proposals?▼

Power curves and sensitivity analyses visualize how power changes across varying effect sizes or sample sizes. This Skill generates these reports to justify sample sizes for grants, IRB protocols, and pre-registration.

Does this power analysis tool support unequal group allocation ratios?▼

Yes, you can specify allocation ratios when groups are unequal. Provide your test type, effect size, target power, alpha, and allocation ratio to compute accurate sample sizes for unbalanced study designs.

What is the difference between closed-form solvers and simulation-based power estimates?▼

Closed-form solvers calculate power analytically for standard tests like t-tests and ANOVA, while simulation-based estimates use Monte Carlo methods for complex designs like logistic models, mixed effects, and survival analysis.

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