ab-testing-framework

Design statistically valid A/B tests with sample size calculations and significance testing.

97|18|Updated Feb 9, 2026

Search Tags:#data-analytics #experimentation #decision-making #ab-testing #sample-size #statistical-significance

Ready to run

npx skills add https://github.com/thatrebeccarae/claude-marketing --skill ab-testing-framework

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: ab-testing-framework
Source: https://github.com/thatrebeccarae/claude-marketing/tree/main/skills/ab-testing-framework
Command: npx skills add https://github.com/thatrebeccarae/claude-marketing --skill ab-testing-framework

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

A standardized framework for planning, running, and interpreting A/B and multivariate experiments to help teams make reliable, data-driven decisions.

Core Features & Use Cases

Hypothesis design templates and best practices for experiments on landing pages, emails, pricing, and product features.
Sample size calculations and significance testing using frequentist or Bayesian approaches.
A decision framework that guides implementation or learning from results while guarding against common pitfalls.

Quick Start

Define your hypothesis and expected lift, choose a test type, and run through sample size calculation and decision rules.

Frequently Asked Questions about ab-testing-framework

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 landing pages?▼

Sample size calculations for A/B testing landing pages require defining your expected lift and choosing a test type. This framework computes the necessary sample size to ensure your experiment reaches statistical significance before interpreting the variant comparison results.

What is the best way to design a hypothesis for pricing experiments?▼

Designing a hypothesis for pricing experiments involves defining the expected change and the metric you are tracking. The framework provides hypothesis design templates and best practices to structure your pricing tests and guard against common pitfalls.

Can I use Bayesian or frequentist approaches for statistical significance testing?▼

Statistical significance testing supports both Bayesian and frequentist approaches. You choose your preferred test type during the design phase, and the framework applies the corresponding significance testing method to evaluate your experiment data.

How do I interpret A/B test results for product features?▼

Interpreting A/B test results for product features applies a structured decision framework to guide implementation based on your data. It provides actionable decision guidance while guarding against common pitfalls like insufficient sample sizes or invalid test setups.

Does this A/B testing framework work for small-scale email experiments?▼

This A/B testing framework works for both small and large-scale email experiments. It applies step-by-step design, sample size calculations, and decision rules to ensure your email variants are compared reliably regardless of scale.