What is A/B Testing?

A/B testing removes guesswork from product and marketing decisions. Instead of debating whether a blue or green button converts better, you test both with real users and let data decide. The process involves forming a hypothesis, designing variants, determining sample size for statistical significance, running the experiment, and analyzing results.

Statistical rigor is critical. Common mistakes include stopping tests too early (reaching false conclusions from insufficient data), testing too many variables simultaneously without proper multivariate design, ignoring novelty effects (users might engage with a new design simply because it's different), and not accounting for seasonal or time-of-day effects.

In case interviews, A/B testing demonstrates data-driven decision-making. If a company wants to improve its website conversion rate, recommending an A/B testing program shows practical thinking. Companies like Google, Netflix, and Amazon run thousands of A/B tests simultaneously—every change is tested rather than assumed to be better.