Statistical Power Calculator

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