ab-test-setup

To calculate A/B test sample size, you need baseline conversion rates, minimum detectable effects, and desired statistical power. This skill provides guidance on these parameters to estimate required sample sizes for reliable experiment results.

Setting up an A/B test plan for pricing experiments requires defining hypotheses, success metrics, and guardrails. This skill generates documentation templates and operational workflows for pricing experiments and feature rollouts.

Use multivariate testing (MVT) instead of a standard A/B test when evaluating multiple variants simultaneously. This skill supports A/B, A/B/n, MVT, and split URL frameworks with guidelines on when each methodology is appropriate.

Before running onboarding flow experiments, you need primary success metrics, secondary metrics, and guardrail metrics. This skill helps craft precise hypotheses and define these metric categories to guide your experiments.

Interpreting A/B test results involves analyzing primary, secondary, and guardrail metrics alongside segment analyses. This skill provides templates for tracking and reporting results to support clear decision-making and learning.

Yes, this A/B testing framework applies to web pages, onboarding flows, pricing experiments, and messaging. It supports hypothesis development, variant creation, execution, and results interpretation across these use cases.