How does this skill improve A/B testing in Claude?

It provides a structured framework for selecting the correct statistical test—such as t-tests or z-tests—and ensures Claude interprets p-values alongside effect sizes for business relevance.

How does it help avoid misleading statistical claims?

It acts as a critical reviewer, flagging potential issues like survivorship bias, the multiple comparisons problem, and the difference between correlation and causation.

Can this skill handle non-normally distributed data?

Yes, it includes specific methodologies for skewed data, recommending robust measures like Medians and Interquartile Ranges (IQR) instead of standard means and deviations.

Does it provide code snippets for data analysis?

The skill includes implementation patterns for common Python libraries to perform moving averages, Z-score calculations, and percentile-based outlier detection.

Statistical Analysis

Name: Statistical Analysis
Author: anthropics

byanthropics

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8,000

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データサイエンスとML

Applies advanced statistical methods to analyze data distributions, detect trends, and validate hypotheses with scientific rigor.

The Statistical Analysis skill empowers Claude to perform sophisticated data interpretation using industry-standard methodologies. It provides structured guidance on descriptive statistics, trend forecasting, and anomaly detection, ensuring that data-driven insights are robust and resistant to common pitfalls like survivorship bias or Simpson's Paradox. Whether you're conducting A/B test evaluations or analyzing complex business metrics, this skill ensures results are presented with proper context, including effect sizes, confidence intervals, and actionable business narratives.

主な機能

01Robust outlier and anomaly detection using Z-score and IQR methodologies

02Advanced trend analysis with moving averages, seasonality detection, and growth rate calculations

03Built-in safeguards against common fallacies like correlation-causation confusion and Simpson's Paradox

04Rigorous hypothesis testing framework for A/B tests and significance validation

05Comprehensive descriptive statistics including central tendency, spread, and percentile reporting

068,000 GitHub stars

ユースケース

01Cleaning and validating datasets by identifying statistical anomalies and data entry errors

02Evaluating A/B test results to determine both statistical significance and practical business impact

03Analyzing historical business metrics to identify seasonal trends and forecast future growth

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add anthropics/knowledge-work-plugins statistical-analysis

For use in Claude.ai and ChatGPT

Download Skill