ab-test-setup

Plan and design A/B tests with hypothesis generation and sample size calculation.

Updated Feb 25, 2026

Search Tags:#analytics #hypothesis testing #experimentation #a/b testing #growth marketing #conversion rate optimization

Ready to run

npx skills add https://github.com/systemifyautomation/claude-marketing-skills --skill ab-test-setup-systemifyautomation

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: ab-test-setup
Source: https://github.com/systemifyautomation/claude-marketing-skills/tree/main/skills/ab-test-setup
Command: npx skills add https://github.com/systemifyautomation/claude-marketing-skills --skill ab-test-setup-systemifyautomation

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill helps you plan, design, and set up A/B tests or experiments to improve conversion rates and optimize user experiences based on data.

Core Features & Use Cases

Hypothesis Formulation: Guides you to create clear, testable hypotheses using a structured framework.
Test Design: Assists in defining test types, sample sizes, and metrics for statistical rigor.
Variant Planning: Provides best practices for designing variants and allocating traffic.
Use Case: You want to test a new headline on your landing page to see if it increases sign-ups. This Skill will help you formulate a hypothesis, determine the necessary sample size, define primary and secondary metrics, and plan the variant content.

Quick Start

Use the ab-test-setup skill to plan an A/B test for the homepage hero section.

Frequently Asked Questions about ab-test-setup

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

FAQPage Schema

How do I formulate a strong hypothesis for A/B testing?▼

A strong A/B testing hypothesis uses a structured framework to define the expected change, the targeted user behavior, and the measurable outcome. This Skill guides you to create clear, testable hypotheses to ensure your conversion rate optimization experiments produce actionable insights.

What's the best way to calculate sample size and define metrics for an experiment?▼

Calculating sample size and defining metrics for an experiment requires statistical rigor to avoid false positives. This Skill assists in determining appropriate test types, calculating necessary sample sizes, and selecting primary and secondary metrics for reliable growth marketing analysis.

How do I plan traffic allocation and variant design for conversion rate optimization?▼

Planning traffic allocation and variant design for conversion rate optimization involves splitting audiences and structuring test variants effectively. This Skill provides best practices for allocating traffic and planning variant content to test changes like a new landing page headline.

Can I use this to plan A/B tests for my landing page sign-ups?▼

Yes, you can use this Skill to plan A/B tests for landing page sign-ups. It helps you formulate a hypothesis, determine necessary sample sizes, define metrics, and plan variant content specifically aimed at increasing user sign-ups through data-driven experimentation.

What is the purpose of setting up A/B tests using a structured framework?▼

Setting up A/B tests using a structured framework ensures statistical rigor and generates actionable insights for data-driven decisions. It supports hypothesis generation, metric selection, and variant design to systematically validate changes and improve conversion rates.