ab-test-setup

To calculate A/B test sample size, you must define your primary conversion metrics, expected effect size, and statistical rigor parameters. This ensures your experiment gathers enough data to detect meaningful changes without false positives.

A strong A/B testing hypothesis uses a structured framework connecting a specific change to a measurable outcome, such as "Changing X will improve Y because Z." This approach ensures your experiment tests one variable and measures what matters.

Setting up A/B test variants involves designing a control experience and a treatment variant with a single, meaningful change. You then allocate traffic between them to isolate the impact of that specific variable on user behavior.

Track primary metrics to measure your main goal, secondary metrics for additional insights, and guardrail metrics to prevent negative impacts on other areas. This combination ensures your A/B test produces statistically valid and actionable results.

Choose server-side A/B testing when you need to test changes deep in your product logic or avoid page flicker, whereas client-side testing suits quick front-end marketing changes. Both require careful traffic allocation to maintain statistical rigor.