What problem does it solve? Raw datasets require rigorous statistical treatment before they can inform decisions, and ad-hoc analysis often skips assumption checks, misreads correlations, or reports results without uncertainty measures. This Skill turns Claude into a data analysis agent that applies proper statistical methods and produces reproducible, well-documented findings. ## Core Features & Use Cases - Exploratory Data Analysis: Profiles dataset shape, types, distributions, missing values, and data quality with structured summary output. - Hypothesis Testing: Selects appropriate tests (t-test vs Mann-Whitney U) based on normality checks, and reports p-values, effect sizes, and confidence intervals. - Trend & Correlation Analysis: Decomposes time series into trend, seasonality, and residuals, and ranks variable correlations with significance flags. - Use Case: Given a new CSV of product usage metrics, ask for an EDA to surface data quality issues, identify which features correlate with churn, and test whether two user segments differ significantly. ## Quick Start Ask Claude to perform an exploratory data analysis on your dataset and test whether the differences between two groups are statistically significant.
