ab-test-setup

Design, plan, and analyze A/B tests with statistical rigor.

1|Updated Mar 11, 2026
One-click install
npx skills add https://github.com/Rollandcodes/BizAI --skill ab-test-setup-rollandcodes
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: ab-test-setup
Source: https://github.com/Rollandcodes/BizAI/tree/main/SKILL.md/skills/ab-test-setup
Command: npx skills add https://github.com/Rollandcodes/BizAI --skill ab-test-setup-rollandcodes

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Design, plan, and analyze statistically rigorous A/B tests to validate product decisions and optimize outcomes.

Core Features & Use Cases

  • Hypothesis framing and test planning to translate intuition into testable statements.
  • Sample size calculations and power analysis to determine required traffic and duration.
  • Variant design guidance across A/B, A/B/n, MVT, and split URL tests, plus documentation templates for plan and results.
  • Interpretation guidance with statistical significance, confidence intervals, and practical impact analysis.

Quick Start

Draft a complete A/B test plan for a target feature including hypothesis, primary metric, expected lift, sample size, traffic split, duration, and decision criteria.

Frequently Asked Questions about ab-test-setup

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

FAQPage Schema
How do I calculate the sample size needed for an A/B test?▼

Sample size calculation for A/B testing requires expected lift, baseline conversion rates, and statistical power analysis to determine required traffic and test duration.

How do I frame a hypothesis for variant testing?▼

Variant testing hypotheses translate product intuition into testable statements, defining primary and secondary metrics to validate product decisions with statistical rigor.

When should I use multivariate testing instead of a standard A/B test?▼

Standard A/B tests validate single variable changes, while MVT evaluates multiple variables simultaneously. Guidance covers variant design across A/B, A/B/n, MVT, and split URL tests.

Why does peeking at A/B test results affect statistical significance?▼

Peeking inflates false positive rates in A/B testing. Proper result interpretation requires predefined decision criteria and protection against premature evaluation of statistical significance.

Can I use this to plan pricing and messaging experiments for web and mobile platforms?▼

Yes, controlled experiments apply to features, pricing, messaging, and UX changes across web and mobile platforms, requiring defined traffic splits and duration.

What is the best way to document an A/B test plan and results?▼

Document A/B test plans and results using templates that capture hypothesis, primary metric, expected lift, sample size, traffic split, duration, and statistical significance interpretation.