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