A/B testing is an experimentation method that consists of showing two versions of the same element (page, feature, email, price) to two randomly assigned groups of users, then comparing which one best achieves a measured goal, such as conversion rate or retention.
The principle is simple, the rigor less so. You need to define the metric before launching the test, calculate the required sample size and wait for enough data for the observed difference to be statistically significant, not due to chance. Stopping a test as soon as it "looks like it's winning" is the most common mistake.
Technically, tests often rely on feature flags to show one variant or the other. Tools such as Optimizely, GrowthBook or Statsig handle assignment and analysis. It is a core tool for product and growth teams.
Why it matters when hiring
A/B testing mainly concerns Product Managers, Data Analysts and Growth Engineers. The good interview signal: a candidate who talks about sample size, significance and a test that surprised them or that they invalidated. The surface-level version is someone who quotes conversion lifts without being able to say how many users or how long the test ran.