Glossary · Growth

A/B testing

Showing two variants to comparable groups and measuring which performs better, on the same event data the rest of your analytics uses.

The value of a test is decided before it runs: a hypothesis, one variant, a metric agreed in advance, and a sample size that makes the result mean something. Tests fail most often not because the variant lost but because the team could not say beforehand what winning looked like. Running the experiment on the same event store as the analytics matters here: if the test tool and the analytics disagree on what a conversion is, the result is unarguable in the worst sense.

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Run it on your own data.