ab-testing

Name: ab-testing
Availability: InStock
Author: tradertunante

Plan and analyze A/B tests with statistically valid results.

Updated Feb 9, 2026

Search Tags:#growth #hypothesis #experimentation #ab-testing #sample-size #conversion-rate #statistical-significance

Ready to run

npx skills add https://github.com/tradertunante/servicecontrol --skill ab-testing-tradertunante

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: ab-testing
Source: https://github.com/tradertunante/servicecontrol/tree/main/.agents/skills/ab-testing
Command: npx skills add https://github.com/tradertunante/servicecontrol --skill ab-testing-tradertunante

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Planning, designing, and analyzing A/B tests and growth experiments often suffers from weak hypotheses, unclear success criteria, and inconsistent documentation. This Skill provides a structured framework to plan, run, and interpret experiments with rigor.

Core Features & Use Cases

Hypothesis-driven experimentation across A/B, A/B/n, and multivariate tests
Sample size calculation, test duration planning, and power analysis based on baseline metrics
Metrics selection guidance (primary, secondary, and guardrails) and result interpretation templates
Reusable templates for test plans, results, and playbooks to speed up experimentation program

Quick Start

Design a test by formulating a clear hypothesis and selecting the primary metric, then use the provided templates to plan and document the test from start to decision.

Frequently Asked Questions about ab-testing

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 with statistical significance?▼

Calculate A/B test sample size by inputting your baseline conversion rate, minimum detectable effect, significance level, and statistical power to determine the exact test duration and valid sample size needed.

What is the best way to structure an A/B testing hypothesis for conversion rate optimization?▼

Structure A/B testing hypotheses using a clear framework that defines the growth experiment, selects primary and secondary metrics, and establishes guardrails to ensure valid and measurable conversion rate optimization results.

Can I run multivariate tests and A/B/n tests using this experimentation framework?▼

Yes, you can run multivariate tests and A/B/n tests using this experimentation framework, which supports planning and analyzing multiple variants to determine the best performing option with statistically valid results.

How do I analyze A/B test results and document performance metrics?▼

Analyze A/B test results by comparing variant performance against your predefined baseline metrics. Use the provided templates to document test plans, performance metrics, and final results for your growth experimentation program.

What metrics should I track when planning a website funnel A/B test?▼

Track primary, secondary, and guardrail metrics when planning a website funnel A/B test. This ensures comprehensive measurement of your homepage, pricing, or signup page variants without negatively impacting overall user experience.

When should I avoid A/B testing and rely on other product management methods?▼

Avoid A/B testing when your website lacks sufficient baseline traffic to reach the required sample size, or when the minimum detectable effect is too small to achieve statistical significance within a viable test duration.