PyStatPower

PyStatPower is a Python package for statistical power analysis that allows users to estimate sample size, test power, and effect size.

简体中文 | English

🗺️ Feature List & Roadmap: To see currently supported statistical models and planned features, check out roadmap.

📦 Installation

Prerequisites: Python 3.10+

pip install pystatpower

🚀 Usage Examples

Sample Size Estimation

• Single Proportion Confidence Interval

  from pystatpower import proportion
  
  size = proportion.single.ci.solve_size(
      proportion=0.9,
      distance=0.10,
      conf_level=0.95,
      interval_type="two-sided",
  )
  print(size)
  
  # output: 158

• Single Proportion Inequality Test

  from pystatpower import proportion
  
  size = proportion.single.inequality.solve_size(
      null_proportion=0.80,
      proportion=0.95,
      alternative="one-sided",
      alpha=0.025,
      power=0.8,
  )
  print(size)
  
  # output: 42

• Two Independent Proportions Non-Inferiority Test

  from pystatpower import proportion
  
  size = proportion.independent.noninferiority.solve_size(
      treatment_proportion=0.95,
      reference_proportion=0.90,
      margin=-0.10,
      ratio=1,
      alpha=0.025,
      power=0.8,
  )
  print(size)
  
  # output: (48, 48)

• Two Independent Means Superiority Test

  from pystatpower import mean
  
  size = mean.independent.superiority.solve_size(
      diff=0.5,
      margin=0.1,
      treatment_std=1.2,
      reference_std=1.2,
      ratio=2,
      alpha=0.025,
      power=0.8,
  )
  print(size)
  
  # output: (214, 107)

Statistical Power Calculation

from pystatpower import proportion

power = proportion.independent.noninferiority.solve_power(
    treatment_proportion=0.95,
    reference_proportion=0.90,
    margin=-0.10,
    treatment_size=48,
    reference_size=48,
    alpha=0.025,
)
print(power)

# output: 0.800282915718918

Effect Size Solving

from pystatpower import proportion

treatment_proportion = proportion.independent.noninferiority.solve_treatment_proportion(
    reference_proportion=0.90,
    margin=-0.10,
    treatment_size=48,
    reference_size=48,
    alpha=0.025,
    power=0.8,
)
print(treatment_proportion)

# output: 0.9499637015276098

🧪 Compatibility Matrix

	🐍 3.10	🐍 3.11	🐍 3.12	🐍 3.13	🐍 3.14
SciPy 1.7	✅	-	-	-	-
SciPy 1.8	✅	-	-	-	-
SciPy 1.9	✅	-	-	-	-
SciPy 1.10	✅	✅	-	-	-
SciPy 1.11	✅	✅	✅	-	-
SciPy 1.12	✅	✅	✅	-	-
SciPy 1.13	✅	✅	✅	-	-
SciPy 1.14	✅	✅	✅	-	-
SciPy 1.15	✅	✅	✅	✅	-
SciPy 1.16	-	✅	✅	✅	✅
SciPy 1.17	-	✅	✅	✅	✅
SciPy 1.18	-	-	✅	✅	✅

Note

- : This combination of Python and SciPy does not exist.

✨ Contributing

Issues and pull requests are welcome and highly appreciated. To get started, check out the contributing guidelines.

🤝 Acknowledgments

• scipy
• pingouin