ckm:ab-test-setup

What problem does it solve?

Designing and running experiments that yield reliable, actionable insights can be hard without a clear framework. This skill provides a structured approach to plan A/B tests and experiments, reducing guesswork and accelerating data-driven decisions.

Core Features & Use Cases

• Hypothesis-driven test design for A/B, A/B/n, and Multivariate Tests (MVT), with guidance on when to use each type.
• Statistical planning and guardrails: sample-size estimation, significance criteria, and safe test duration to prevent premature conclusions.
• End-to-end execution, analysis, and learning: documentation templates, result interpretation, and an approach to building a shared learning repository.

Quick Start

Draft a test plan by stating a hypothesis, choosing a test type, outlining variants, and defining the primary metric.