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ab-testing

Design and analyze A/B tests with hypothesis-driven experimentation and statistical interpretation.

2|Updated Apr 25, 2026
Search Tags:#statistics#hypothesis#experimentation#ab-testing#sample-size#ice-framework#post-test
One-click install
npx skills add https://github.com/viethahong/business-skills --skill ab-testing-viethahong
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: ab-testing
Source: https://github.com/viethahong/business-skills/tree/main/skills/research/ab-testing
Command: npx skills add https://github.com/viethahong/business-skills --skill ab-testing-viethahong

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Design and analyze A/B tests to make data-driven decisions.

Core Features & Use Cases

  • ICE-based prioritization to select test ideas.
  • Hypothesis engineering and experimental design.
  • Sample size calculations and statistical significance checks.
  • End-to-end post-test analysis and learning repository.
  • Cross-functional applicability to marketing, product, and growth experiments.

Quick Start

Define your first hypothesis, select a single variable to test, and outline the primary metric and success criteria.

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?▼

A/B test sample size calculation requires defining your primary metric, expected effect size, and success criteria. The Skill provides an Experiment Parameters template to plan sample size and ensure your test reaches statistical significance.

What is the ICE framework for prioritizing experiments?▼

The ICE framework scores test ideas on Impact, Confidence, and Ease. The Skill includes an ICE Scoreboard artifact to quantitatively prioritize which experiment hypotheses to execute first.

How do I write a hypothesis for an A/B test?▼

Writing an A/B test hypothesis involves defining a single variable to test, outlining the primary metric, and setting success criteria. The Skill provides a structured Test Brief and Context Checklist to engineer your hypothesis.

Can I use this for marketing and product growth experiments?▼

Yes, A/B testing applies cross-functionally to marketing, product, and growth experiments. The Skill supports any hypothesis-driven testing scenario requiring statistical interpretation and post-test analysis.

How do I analyze statistical significance after an A/B test?▼

Analyzing statistical significance involves interpreting post-test results against your experiment parameters. The Skill provides a Post-test Report template to document learnings and build an experimentation repository.

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