Statistical Analyst

Plugin Verified Active

Hypothesis testing, A/B experiment analysis, sample size calculation, and confidence intervals. 3 stdlib-only Python tools with Z-test, t-test, chi-square, effect sizes, power analysis, and Wilson score intervals.

1 Skill 0 MCPs

Purpose

To empower users to make data-driven decisions by providing rigorous statistical analysis capabilities, ensuring that observed differences are real and experiments are designed and interpreted effectively.

Features

Perform Z-tests, t-tests, and Chi-square tests
Calculate effect sizes (Cohen's d, h, Cramér's V)
Compute sample sizes for A/B tests
Calculate confidence intervals for proportions and means
Interpret statistical significance with practical context

Use Cases

Analyzing A/B test results to determine if a change had a statistically significant impact.
Calculating the necessary sample size before launching an experiment to ensure it will be conclusive.
Interpreting existing statistical numbers to understand their significance and practical implications.
Validating whether observed differences in data are due to chance or a real effect.

Non-Goals

Designing or instrumenting A/B tests (use dedicated experiment design skills).
Cleaning or validating raw input data (use data quality tools first).
Performing Bayesian inference or multi-armed bandit analysis (these tests are frequentist).
Providing advanced statistical modeling beyond basic hypothesis testing and sample size calculations.

Installation

First, add the marketplace

/plugin marketplace add alirezarezvani/claude-skills

/plugin install statistical-analyst@claude-code-skills

Contains 1 extensions

Skill (1)

Statistical Analyst Skill

Run hypothesis tests, analyze A/B experiment results, calculate sample sizes, and interpret statistical significance with effect sizes. Use when you need to validate whether observed differences are real, size an experiment correctly before launch, or interpret test results with confidence.