statistical-analyst

Apply frequentist hypothesis tests to experimental data with p-values and effect sizes.

Updated Apr 16, 2026

Search Tags:#hypothesis-testing #statistical-analysis #effect-size #p-value #ab-test #confidence-interval

Ready to run

npx skills add https://github.com/devCharuzu/philfida-taskmanage --skill statistical-analyst-devcharuzu

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: statistical-analyst
Source: https://github.com/devCharuzu/philfida-taskmanage/tree/main/.windsurf/skills/statistical-analyst
Command: npx skills add https://github.com/devCharuzu/philfida-taskmanage --skill statistical-analyst-devcharuzu

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

Run hypothesis tests, analyze A/B experiment results, calculate sample sizes, and interpret statistical significance with effect sizes to help teams distinguish real differences from noise and plan robust experiments.

Core Features & Use Cases

Mode 1 — Analyze Experiment Results: interpret results of completed experiments and report significance, effect size, and practical impact.
Mode 2 — Size an Experiment: compute required sample sizes before launch and verify power assumptions.
Mode 3 — Interpret Existing Numbers: evaluate reported metrics and translate statistics into actionable business decisions.

Quick Start

Provide your experiment data (group sizes, observed conversions or means) and run the appropriate tool to obtain p-values, confidence intervals, and recommended actions.

Frequently Asked Questions about statistical-analyst

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I run hypothesis testing on A/B test results to check for statistical significance?▼

Hypothesis testing on A/B test results applies frequentist tests to your control and treatment data, computing p-values and confidence intervals to determine whether observed differences are statistically significant rather than random noise.

What is the best way to calculate required sample size before launching an experiment?▼

Calculating required sample size before launch involves verifying power assumptions and computing group sizes needed to detect a meaningful effect, ensuring your experiment is robust enough to yield statistically significant results.

Can I interpret existing reported metrics without running a new experiment?▼

Interpreting existing reported metrics evaluates already-collected proportions, means, or categories, translating statistics into actionable business decisions by applying hypothesis tests and computing confidence intervals post-hoc.

Does this approach support analyzing both conversion proportions and continuous means?▼

Analyzing both conversion proportions and continuous means is supported, applying frequentist hypothesis tests across different data types to compute p-values, confidence intervals, and effect sizes for comprehensive post-hoc analysis.

Why does my A/B test show statistical significance but no practical business impact?▼

A/B test statistical significance without practical impact occurs when p-values are low but effect sizes like Cohen's d or h are negligible, meaning the detected difference is real but too small to justify business decisions.