ab-test-setup

To calculate A/B test sample size and duration, you input your baseline conversion rate, daily traffic, and minimum detectable effect to generate required sample sizes and sensible test durations using reference tables and calculators.

When designing an A/B test experiment, you should track primary metrics for your main hypothesis, secondary metrics for additional insights, and guardrail metrics to prevent negative impacts on other areas of your product.

To structure a hypothesis for variant design, you define the variant change, provide the rationale behind it, and specify the primary metric to measure, ensuring your hypothesis framework is clear, testable, and statistically valid.

Yes, you can use this for both product and marketing experiment design. It applies to product, marketing, and UX scenarios, covering traffic allocation strategies, implementation guidance, and result analysis across these contexts.

Pre-launch QA for an A/B test involves completing tracking verification, reviewing client and server implementation considerations, and checking variant design and traffic allocation strategies to ensure tracking is accurate before the experiment goes live.

To analyze A/B test results, you follow the analysis checklist to interpret the data, evaluate the primary metric against your hypothesis, and determine whether the results are statistically reliable enough to implement the winning variant.