ab-test-plan

Plan A/B and multivariate experiments with statistical specifications and monitoring.

726|123|Updated Feb 11, 2026

Search Tags:#hypothesis #sample-size #ab testing #conversion-rate-optimization #experiment-design #multivariate

Ready to run

npx skills add https://github.com/indranilbanerjee/digital-marketing-pro --skill ab-test-plan

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: ab-test-plan
Source: https://github.com/indranilbanerjee/digital-marketing-pro/tree/main/skills/ab-test-plan
Command: npx skills add https://github.com/indranilbanerjee/digital-marketing-pro --skill ab-test-plan

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Design and document rigorous A/B and multivariate tests with a structured framework to ensure reliable decision-making and robust experimentation.

Core Features & Use Cases

Hypothesis-driven planning: Define test objectives, success criteria, and rationale to guide experiments.
Statistical planning: Compute required sample sizes, power, alpha, and minimum detectable effect, plus estimate test duration.
Variant design & monitoring: Specify control and treatment variants, implementation details, metrics to track, and stopping rules.
Use Case: CRO scenario spanning a landing page, pricing page, and email subject line to illustrate end-to-end planning.

Quick Start

Provide baseline metrics and traffic figures for your page or asset, then request a full test plan.

Frequently Asked Questions about ab-test-plan

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

FAQPage Schema

How do I calculate sample size and test duration for an A/B test?▼

To calculate A/B test sample size and duration, you need baseline conversion rates, minimum detectable effect, statistical power, and alpha. Provide baseline metrics and traffic figures to compute required sample sizes and estimate test duration for rigorous experiment design.

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

A strong A/B testing hypothesis for conversion rate optimization defines clear test objectives, success criteria, and rationale. Structuring hypotheses this way guides experiments and ensures reliable decision-making across landing pages, pricing, and messaging assets.

Can I use this approach to plan multivariate experiments, or is it only for simple A/B tests?▼

Yes, you can plan multivariate experiments alongside simple A/B tests. The framework specifies control and treatment variants, implementation details, metrics to track, and stopping rules for both test types to ensure statistical rigor.

What metrics and monitoring rules do I need to set up before launching an experiment?▼

Before launching an experiment, define metrics to track, stopping rules, and governance protocols. Specifying these monitoring parameters alongside variants and implementation details prevents premature conclusions and ensures robust experimentation.

Does A/B test planning work for email subject line and checkout optimization?▼

A/B test planning works for email subject line and checkout optimization by applying structured hypothesis-driven frameworks. It calculates required sample sizes and estimates test duration across web CRO, product experiments, and messaging assets.

When should I not use A/B testing for my conversion rate optimization?▼

You should not use A/B testing for conversion rate optimization when traffic is too low to achieve statistical power within a reasonable duration. Without sufficient baseline traffic, computing required sample sizes yields impractical test periods and unreliable results.