What is A/B Testing?

A/B testing is the practice of presenting two variants of something to similar audiences simultaneously and measuring which performs better against a defined goal. In a web context, this typically means showing 50% of users a control version (A) and 50% a variation (B) and tracking which version produces more conversions, clicks, or whatever metric you're trying to improve.

The core value of A/B testing is that it replaces opinion with data. Instead of debating whether a red button or a blue button will convert better, you run a test and let user behavior answer the question definitively. The discipline of doing this rigorously requires understanding statistical significance — a test needs enough data to be confident the difference in performance is real and not just random variation.

A/B testing can be applied to almost anything: email subject lines, ad copy, pricing page layouts, checkout flows, product descriptions, CTA button text, form length, page headlines, and more. Multivariate testing tests multiple variables simultaneously, though it requires significantly more traffic to reach statistical significance. Most testing tools (like Google Optimize, VWO, Optimizely, or Unbounce) handle the traffic splitting and statistical analysis automatically.