ab-test-setup

To frame an A/B test hypothesis, define the expected change, the target metric, and the minimum detectable effect. This Skill guides you through structuring hypotheses for product, marketing, and UX experiments before variant design.

A/B/n testing compares multiple independent variants against a control, while MVT evaluates interactions between multiple elements simultaneously. The Skill helps select the right test type based on your experimentation scope and traffic constraints.

Select primary metrics to measure the main test goal, secondary metrics for additional insights, and guardrail metrics to prevent negative impacts on user experience. The Skill provides a framework for choosing and interpreting all three metric types.

Yes, the framework supports split URL tests alongside A/B, A/B/n, and MVT tests. It provides variant design guidance and traffic allocation strategies to distribute visitors effectively across test variations.

Interpret statistical significance by evaluating whether the observed variant performance meets your predefined threshold. The Skill includes full test lifecycle documentation templates and governance to record outcomes comprehensively.