19-ab-test-setup

To calculate A/B test sample size, you need your baseline conversion rate and minimum detectable effect. This Skill computes the required traffic volume to reach statistical significance and determine a clear winner.

Setting up an A/B test requires explicit hypotheses, baseline conversion metrics, sample size calculations, experiment duration, and tracking across web traffic, email lists, or paid media to ensure measurable results.

A/B test duration depends on your computed sample size and daily traffic volume. Typically, running tests for 7 to 14 days with 50/50 traffic split ensures you capture enough data for statistical significance.

Yes, A/B testing applies to emails and product prompts. You can test variants across email lists and paid media using measurable metrics like CTR, conversion rate, and revenue to identify the winning variant.

The best way to document A/B test results is recording hypotheses, baseline metrics, and conclusions in a standardized template. This ensures governance and supports repeatable optimization across campaigns.

An A/B test may lack a clear winner if the sample size is too small, the minimum detectable effect is poorly defined, or the experiment duration is insufficient to reach statistical significance.