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ab-test-plan

Generate A/B and multivariate test plans with hypothesis framing and sample size calculations.

Updated May 18, 2026
Search Tags:#experiment design#hypothesis#a/b testing#sample size#cro#conversion rate optimization
One-click install
npx skills add https://github.com/ajayatwal1105-emerson/digital-marketing-pro --skill ab-test-plan-ajayatwal1105-emerson
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: ab-test-plan
Source: https://github.com/ajayatwal1105-emerson/digital-marketing-pro/tree/main/skills/ab-test-plan
Command: npx skills add https://github.com/ajayatwal1105-emerson/digital-marketing-pro --skill ab-test-plan-ajayatwal1105-emerson

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill removes guesswork from A/B testing by turning an experiment idea into a statistically valid plan with clear success criteria.

Core Features & Use Cases

  • Hypothesis-driven experiment design: Builds an If/Then/Because hypothesis tied to measurable outcomes.
  • Sample size and duration planning: Calculates required sample sizes and estimates run length based on traffic.
  • Decision-ready monitoring and stopping rules: Defines guardrails, interim QA checks, SRM detection, and go/no-go thresholds.
  • Experiment documentation: Produces a pre-registration style plan suitable for campaign tracking and auditability.

Quick Start

Provide the element to test, baseline conversion rate, desired MDE, daily traffic, and your preferred confidence/power, then ask for the complete A/B test plan with variants, metrics, sample size, duration, and stopping rules.

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 for an A/B test on my landing page?▼

You generate a conversion rate optimization experiment plan by defining an If/Then/Because hypothesis tied to a measurable outcome, specifying variant definitions, and establishing guardrails for SRM detection and quality assurance checks.

What statistical parameters do I need for rigorous experiment design?▼

Decision-ready A/B test monitoring requires guardrails, interim QA checks, SRM detection, and go/no-go thresholds to ensure valid results and prevent premature stopping during conversion rate optimization experiments.

Can I use this for multivariate test design across checkout flows?▼

Pre-registration documentation for A/B testing produces an auditable experiment plan containing hypothesis framing, variant definitions, sample size calculations, and stopping rules suitable for campaign tracking.

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

An A/B test plan generates hypothesis framing, variant definitions, sample size calculations, and pre-registration documentation, whereas generic CRO strategy focuses on broader heuristic analysis and user research without strict statistical parameters.

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