Can this skill help me interpret p-values?

Absolutely. It provides guidance on defining null hypotheses, choosing significance levels (alpha), and interpreting p-values and effect sizes correctly to avoid common pitfalls like 'correlation equals causation'.

Is this skill suitable for non-data scientists?

Yes, it provides clear, reusable code patterns and best practices that help developers and product managers perform rigorous analysis without a deep background in statistics.

What statistical methods does this skill support?

The skill supports a wide range of methods including A/B testing, hypothesis testing (such as Chi-square), linear regression, correlation analysis, and predictive modeling.

Does this skill work with Python data science libraries?

Yes, it provides optimized patterns and best practices for using popular Python libraries like Pandas, SciPy, and Scikit-learn for statistical computations.

Statistical Analysis Skill

Name: Statistical Analysis Skill
Author: spjoshis

byspjoshis

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Data Science & ML

Applies advanced statistical methods including hypothesis testing, A/B testing, and regression analysis to derive data-driven insights.

About

The Statistical Analysis skill equips Claude with the specialized expertise required to perform rigorous data analysis and predictive modeling. By providing standardized implementation patterns for A/B tests, correlation studies, and regression models, this skill enables developers to automate the calculation of p-values, significance levels, and lift metrics using industry-standard libraries like SciPy and Scikit-learn. It is particularly useful for teams needing to validate product changes, forecast trends, or detect anomalies within large datasets, ensuring every technical decision is backed by statistically sound evidence.

Key Features

Predictive modeling using linear and multi-variable regression
Correlation and trend analysis for business forecasting
Hypothesis testing frameworks including Chi-square and T-tests
Automated A/B test significance and lift calculations
1 GitHub stars
Validation of statistical assumptions and anomaly detection

Use Cases

Predicting future sales or user growth using regression models
Analyzing marketing campaign performance through A/B testing
Validating the impact of product feature releases with significance testing

About

Key Features

Predictive modeling using linear and multi-variable regression
Correlation and trend analysis for business forecasting
Hypothesis testing frameworks including Chi-square and T-tests
Automated A/B test significance and lift calculations
1 GitHub stars
Validation of statistical assumptions and anomaly detection

Use Cases

Predicting future sales or user growth using regression models
Analyzing marketing campaign performance through A/B testing
Validating the impact of product feature releases with significance testing