biostatistics

name	biostatistics
description	Medical biostatistics hypothesis testing toolkit: t-tests, ANOVA, chi-square, Fisher exact, Mann-Whitney, Kruskal-Wallis, sample size calculation, power analysis, multiple testing correction, survival analysis, and clinical trial biostatistics.
tags	["biostatistics","hypothesis-testing","clinical-trials","statistics","sample-size","power-analysis","zorai"]

Overview

Medical biostatistics for hypothesis testing, clinical trial design, and survival analysis. Covers t-tests, ANOVA, chi-square, Fisher exact, Mann-Whitney, Kruskal-Wallis, sample size calculation, power analysis, and multiple testing correction.

Installation

uv pip install scipy statsmodels

Common Tests

import numpy as np
from scipy import stats

# Two-sample t-test
t_stat, p = stats.ttest_ind(np.random.normal(100, 15, 30), np.random.normal(110, 15, 30))

# Mann-Whitney
u_stat, p = stats.mannwhitneyu(np.random.normal(100, 15, 30), np.random.normal(110, 15, 30))

# Chi-square
chi2, p, dof, _ = stats.chi2_contingency(np.array([[30, 10], [20, 40]]))

Sample Size

from statsmodels.stats.power import TTestIndPower
n = TTestIndPower().solve_power(effect_size=0.5, power=0.80, alpha=0.05)
print(f"N per group: {np.ceil(n):.0f}")

References

• SciPy stats
• statsmodels