Calculate statistical power and determine required sample sizes for hypothesis testing and experimental design.
Purpose
Experiment planning for:
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Clinical trial design
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A/B test planning
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Research study sizing
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Survey sample size determination
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Power analysis and validation
Features
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Power Calculation: Calculate statistical power for tests
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Sample Size: Determine required sample size for desired power
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Effect Size: Estimate detectable effect size
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Multiple Tests: t-test, proportion test, ANOVA, chi-square
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Visualizations: Power curves, sample size charts
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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