A/B Testing
Comparing two versions of a design by showing each to a slice of live traffic and measuring which drives the target metric better.
An A/B test splits live users between a control (A) and one or more variants (B), then measures which produces more of the behavior you care about — signups, purchases, upgrades. Because the comparison happens on real traffic at the same time, it isolates the effect of the change from seasonality and noise.
A/B testing is the engine of conversion rate optimization and growth design. Its main traps are calling results before reaching statistical significance and testing changes too small to matter — discipline about sample size and hypothesis quality is what separates signal from superstition.
Related terms
Conversion Rate Optimization (CRO)The systematic practice of increasing the share of users who take a desired action, through testing and iterative improvement of the experience.Growth DesignDesign practiced as a growth lever — every flow, screen, and experiment aimed explicitly at activation, conversion, and retention metrics.Conversion FunnelThe staged path users take toward a goal — visit, sign up, activate, pay — where measuring drop-off at each step reveals where to improve.ActivationThe point at which a new user first experiences the core value of a product — the 'aha moment' that predicts whether they'll stick around.