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数据与复现
DA
data-stats-analysis
维护者 FreedomIntelligence · 最近更新 2026年4月1日
data-stats-analysis：This skill enables you to perform rigorous statistical analyses ，涵盖 t-tests，ANOVA，correlation analysis，hypothesis testing，、 multiple testing corrections。 Unlike cloud-hosted solutions，this skill uses standard Python statistical libraries (**scipy**，**statsmodels**，**numpy**) 、 executes **locally** in your environment，making it compatible ，支持 **ALL LLM providers** ，涵盖 GPT，Gemini，Claude，Dee…。
原始来源
FreedomIntelligence/OpenClaw-Medical-Skills
https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/main/skills/data-stats-analysis
- 维护者
- FreedomIntelligence
- 许可
- MIT
- 最近更新
- 2026年4月1日
技能摘要
来自 SKILL.md 的关键信息
核心说明
- 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)")。
原始文档
SKILL.md 摘录
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)")
适用场景
- 比较 means between groups (t-tests，ANOVA)。
- Test ，用于 correlations between variables。
- 执行 hypothesis testing ，支持 p-value calculation。
- Apply multiple testing corrections (FDR，Bonferroni)。
不适用场景
- Do not rely on this catalog entry alone ，用于 installation 或 maintenance details。
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