biostatistics
SkillDev toolsMedical 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.
Available today. Use it from your connected AI after setup.
No other account needed.
Connect ahel once, and every AI you use reads what you have installed.
Then ask your AI: use the biostatistics skill
What this skill tells your AI
The instructions your AI receives, as published by mkurman/zorai in skills/scientific-skills/biostatistics/SKILL.md and read by ahel’s review.
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
Signals
- GitHub stars
- 324
- Forks
- 26
- Last commit
- Sep 2026
Advanced
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- Gateway key
biostatistics- Source
- github.com/mkurman/zorai