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name: statistical-power-calculator description: Use when asked to calculate statistical power, determine sample size, or plan experiments for hypothesis testing.

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

Copyfrom 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

Copy# 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