ab-test-setup

Plan statistically valid A/B tests with sample sizes and significance levels.

2|Updated Jan 9, 2026
One-click install
npx skills add https://github.com/NammDev/Goads-Krea --skill ab-test-setup-nammdev
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: ab-test-setup
Source: https://github.com/NammDev/Goads-Krea/tree/main/.claude/skills/ab-test-setup
Command: npx skills add https://github.com/NammDev/Goads-Krea --skill ab-test-setup-nammdev

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

A/B testing and experimentation help teams make data-driven decisions by validating changes before broad deployment.

Core Features & Use Cases

  • Guided test design: From hypothesis to variant selection, ensuring single-variable changes.
  • Statistical rigor: Pre-defined sample sizes, significance thresholds, and guardrails.
  • Learning and documentation: Structured test documentation and learning repository.

Quick Start

Design a statistically valid A/B test plan for the given scenario and provide clear actionable recommendations.

Frequently Asked Questions about ab-test-setup

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

FAQPage Schema
How do I design a statistically valid A/B test for product changes?

Calculate A/B test sample size by determining your baseline conversion rate, minimum detectable effect, desired statistical significance level, and power to ensure your experiment runs long enough for valid results.

What is the right way to calculate A/B test sample size and significance?

Calculate A/B test sample size by determining your baseline conversion rate, minimum detectable effect, desired statistical significance level, and power to ensure your experiment runs long enough for valid results.

How do I structure an A/B testing hypothesis for marketing experiments?

Apply A/B testing experimentation to marketing copy by isolating single-variable changes across variants, establishing guardrails, and documenting the structured test plan and result interpretation for your learning repository.

Can I use A/B testing experimentation for both marketing copy and product features?

Interpret A/B test results by evaluating data collection against pre-defined statistical significance thresholds and power requirements, then documenting causal impact and actionable recommendations in a structured learning repository.

Why do my A/B test results show conflicting causal impact across different metrics?

Avoid invalid A/B tests by isolating single-variable changes, pre-defining sample sizes and significance thresholds, and preventing premature data evaluation before the experiment timeline concludes.