ab-test-setup

To calculate sample size and duration for an A/B test, you need baseline data and your primary metric. This skill provides sample size guidance based on varying traffic levels and contexts to ensure your experiment reaches statistical rigor.

The best way to plan a multivariate experiment is through hypothesis-driven test planning. This skill provides structured variant design templates and comprehensive metrics frameworks to help you execute MVTs and document results effectively.

Writing a strong hypothesis for growth experiments requires defining the change, the expected outcome, and the baseline data. This skill provides structured hypothesis templates to ensure your growth experiments are designed with statistical rigor.

Yes, you can use this for both client-side and server-side implementations. The skill supports A/B, A/B/n, MVT, and split URL tests, providing variant design and measurement governance across different product marketing and website optimization contexts.

You need a metrics framework for split URL tests to accurately track primary and secondary metrics against baseline data. This structured approach ensures your website optimization results are statistically valid and can be documented into a reusable experimentation playbook.