mcpField
S
statistical-power-calculator

statistical-power-calculator

github.com/dkyazzentwatwaData & databases12458 installsUpdated 29w ago
Repository

About statistical-power-calculator

statistical-power-calculator is a tool for planning hypothesis tests and experiments by calculating statistical power, sample sizes, and detectable effect sizes. It supports several test types and produces visualizations and analysis reports.

Key features

  1. 01Calculate statistical power
  2. 02Determine required sample sizes
  3. 03Estimate detectable effect sizes
  4. 04Support t-tests, proportion tests, ANOVA, and chi-square
  5. 05Plot power curves and sample size charts
  6. 06Generate analysis reports with recommendations

Use cases

  1. 01Plan sample sizes for clinical trials
  2. 02Size A/B tests before launch
  3. 03Estimate survey sample requirements
  4. 04Evaluate power for research study designs

Install

Copy the command and run it in a terminal.

$npx skills add https://github.com/dkyazzentwatwa/chatgpt-skills --skill statistical-power-calculator

After installing, type/statistical-power-calculatorin Claude Code to confirm it loaded.

Bundled filesscripts/requirements.txtscripts/statistical_power_calculator.py

Details

From the project README

Statistical Power Calculator

Calculate statistical power and determine required sample sizes for hypothesis testing and experimental design.

Purpose

Experiment planning for:

  • Clinical trial design
  • A/B test planning
  • Research study sizing
  • Survey sample size determination
  • Power analysis and validation

Features

  • Power Calculation: Calculate statistical power for tests
  • Sample Size: Determine required sample size for desired power
  • Effect Size: Estimate detectable effect size
  • Multiple Tests: t-test, proportion test, ANOVA, chi-square
  • Visualizations: Power curves, sample size charts
  • Reports: Detailed analysis reports with recommendations

Quick Start

from statistical_power_calculator import PowerCalculator

# Calculate required sample size
calc = PowerCalculator()
result = calc.sample_size_ttest(
    effect_size=0.5,
    alpha=0.05,
    power=0.8
)
print(f"Required n per group: {result.n_per_group}")

# Calculate power
power = calc.power_ttest(n_per_group=100, effect_size=0.5, alpha=0.05)

CLI Usage

# Calculate sample size for t-test
python statistical_power_calculator.py --test ttest --effect-size 0.5 --power 0.8

# Calculate power
python statistical_power_calculator.py --test ttest --n 100 --effect-size 0.5

Comments

Markdown supported