Data & ReproStatistics & Data AnalysisFreedomIntelligence/OpenClaw-Medical-SkillsData & Reproduction

data-stats-analysis

Maintainer FreedomIntelligence · Last updated April 1, 2026

This skill enables you to perform rigorous statistical analyses including t-tests, ANOVA, correlation analysis, hypothesis testing, and multiple testing corrections. Unlike cloud-hosted solutions, this skill uses standard Python statistical libraries (**scipy**, **statsmodels**, **numpy**) and executes **locally** in your environment, making it compatible with **ALL LLM providers** including GPT, Gemini, Claude, Dee….

OpenClawNanoClawAnalysisReproductiondata-stats-analysis📊 data science & toolsstatistics & data analysisperform

Original source

FreedomIntelligence/OpenClaw-Medical-Skills

https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/main/skills/data-stats-analysis

Maintainer: FreedomIntelligence
License: MIT
Last updated: April 1, 2026

Skill Snapshot

Key Details From SKILL.md

2 min

Key Notes

if abs(r_pearson) < 0.3: strength = "weak" elif abs(r_pearson) < 0.7: strength = "moderate" else: strength = "strong.
direction = "positive" if r_pearson > 0 else "negative" print(f"Interpretation: {strength} {direction} correlation").
if p_pearson < 0.05: print("✅ Statistically significant (p < 0.05)") else: print("❌ Not statistically significant").
if p_sw < 0.05: print("❌ Data does NOT follow normal distribution (p < 0.05)") print("→ Recommendation: Use non-parametric tests (Mann-Whitney, Kruskal-Wallis)") else: print("✅ Data appears normally distributed (p >= 0.05)") print("→ OK to use parametric tests (t-test, ANOVA)").

Source Doc

Excerpt From SKILL.md

Overview

This skill enables you to perform rigorous statistical analyses including t-tests, ANOVA, correlation analysis, hypothesis testing, and multiple testing corrections. Unlike cloud-hosted solutions, this skill uses standard Python statistical libraries (scipy, statsmodels, numpy) and executes locally in your environment, making it compatible with ALL LLM providers including GPT, Gemini, Claude, DeepSeek, and Qwen.

When to Use This Skill

Compare means between groups (t-tests, ANOVA)
Test for correlations between variables
Perform hypothesis testing with p-value calculation
Apply multiple testing corrections (FDR, Bonferroni)
Calculate statistical summaries and confidence intervals
Test for normality and distribution fitting
Perform non-parametric tests (Mann-Whitney, Kruskal-Wallis)

Perform independent t-test

t_statistic, p_value = ttest_ind(group1, group2)

print(f"t-statistic: {t_statistic:.4f}") print(f"p-value: {p_value:.4e}")

if p_value < 0.05: print("✅ Significant difference between groups (p < 0.05)") else: print("❌ No significant difference (p >= 0.05)")

Use cases

Compare means between groups (t-tests, ANOVA).
Test for correlations between variables.
Perform hypothesis testing with p-value calculation.
Apply multiple testing corrections (FDR, Bonferroni).

Not for

Do not rely on this catalog entry alone for installation or maintenance details.

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