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ab-test-setup

Design statistically valid A/B testing plans and evaluate experiment results.

Updated Apr 2, 2026
Search Tags:#conversion rate#hypothesis#experimentation#sample size#ab testing#statistical significance#multivariate testing
One-click install
npx skills add https://github.com/gyalamanch001a/pur-new --skill ab-test-setup-gyalamanch001a
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: ab-test-setup
Source: https://github.com/gyalamanch001a/pur-new/tree/main/.claude/skills/ab-test-setup
Command: npx skills add https://github.com/gyalamanch001a/pur-new --skill ab-test-setup-gyalamanch001a

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill helps you design, size, and evaluate experiments so you can make confident product decisions instead of guessing from early or noisy results.

Core Features & Use Cases

  • Experiment Design: Turn a vague idea into a clear A/B, A/B/n, or multivariate test plan with a strong hypothesis.
  • Measurement Planning: Choose primary, secondary, and guardrail metrics that match the business goal and avoid misleading conclusions.
  • Sample Size and Duration: Estimate how much traffic and how long the test needs to run based on baseline conversion and expected lift.
  • Result Interpretation: Decide whether a test is a winner, loser, or inconclusive using statistical significance and practical impact.
  • Use Case: You want to test a new pricing-page CTA, compare four button colors, or determine whether a signup form change is worth shipping.

Quick Start

Use the ab-test-setup skill to create a test plan for my current experiment, including the hypothesis, variant structure, metrics, sample size needs, and recommended run duration.

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 sample size for an A/B test?▼

To calculate A/B test sample size, you need the baseline conversion rate and expected lift. This Skill estimates required traffic and duration based on those inputs to ensure statistical significance.

What is the difference between A/B/n and multivariate testing?▼

A/B/n compares three or more distinct variants, while multivariate testing evaluates multiple changes simultaneously. This Skill designs plans for both scenarios to identify winning combinations.

How do I write a strong hypothesis for conversion rate optimization?▼

A strong conversion rate optimization hypothesis connects a specific change to an expected outcome. This Skill turns vague ideas into clear test plans with defined metrics and variants.

When should I use guardrail metrics in experimentation?▼

Use guardrail metrics in experimentation to prevent misleading conclusions and protect business goals. This Skill helps you choose primary, secondary, and guardrail metrics for valid results.

How do I interpret statistical significance in A/B testing results?▼

Interpreting statistical significance determines if a test is a winner, loser, or inconclusive. This Skill evaluates experiment results using significance and practical impact for decision making.

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