A/B testing (split testing) randomly shows different variants to comparable groups of users and measures which one better achieves a goal — sign-ups, clicks or purchases. It turns design and copy debates into data-driven decisions.
Reliable A/B tests require enough traffic for statistical significance and a single clear variable per test. Misread or underpowered tests are a common source of false conclusions.