ab-testing

Guide A/B testing from hypothesis development through analysis and experimentation programs.

1|Updated May 16, 2026
One-click install
npx skills add https://github.com/cengo33/hal-piyasa --skill ab-testing-cengo33
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: ab-testing
Source: https://github.com/cengo33/hal-piyasa/tree/main/_skills/ab-testing
Command: npx skills add https://github.com/cengo33/hal-piyasa --skill ab-testing-cengo33

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill provides comprehensive A/B testing guidance, from hypothesis development to analysis, to help users optimize conversion rates and build effective growth experimentation programs.

Core Features & Use Cases

  • Hypothesis Framework: Offers a structured approach to forming testable hypotheses.
  • Test Types: Explains different test types (A/B, A/B/n, MVT, Split URL) and their application.
  • Sample Size Calculation: Provides tools and guidance for determining the required sample size.
  • Metrics Selection: Assists in choosing primary, secondary, and guardrail metrics.
  • Experimentation Program: Walks users through setting up a continuous experimentation program.

Quick Start

Run an A/B test on your website by following the structured hypothesis framework and metric selection guidelines provided in the SKILL.md file.

Frequently Asked Questions about ab-testing

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

FAQPage Schema
How do I design an A/B test for conversion optimization?▼

Design A/B testing for conversion optimization by forming a structured hypothesis, selecting primary and guardrail metrics, then calculating the required sample size to ensure valid results.

What is the difference between A/B/n, MVT, and Split URL testing?▼

A/B/n, MVT, and Split URL testing differ in scope: A/B/n tests multiple variations, MVT isolates multiple element combinations, and Split URL redirects to entirely different page designs.

How do I calculate sample size for growth experimentation?▼

Calculate sample size for growth experimentation using the provided tools to input baseline conversion rates, minimum detectable effect size, and desired statistical significance levels.

Do I need prior statistics knowledge to run an A/B test?▼

Yes, conducting A/B testing requires prior knowledge of statistical principles to correctly interpret statistical significance, evaluate metrics, and perform accurate test analysis.

What are guardrail metrics and why do I need them for A/B testing?▼

Guardrail metrics in A/B testing protect overall business health by monitoring secondary indicators like revenue or retention, preventing negative impacts while optimizing primary conversion metrics.

How do I set up a continuous experimentation program?▼

Set up a continuous experimentation program by standardizing the hypothesis framework, test type selection, and metrics guidelines to systematically run, analyze, and iterate on growth tests.