A/B Testing
A method comparing two versions of a webpage or element to determine which performs better statistically.
Definition
A/B testing (also called split testing) is an experimental method where two variants—A (control) and B (variation)—are shown to different user segments simultaneously to determine which performs better on a defined metric. Traffic is randomly split between versions to ensure statistically valid comparison.
Tests can range from simple (button color, headline copy) to complex (entirely different page layouts). Statistical significance calculations determine when enough data has been collected to confidently declare a winner, avoiding false conclusions from random variation.
Why It Matters
A/B testing removes guesswork from optimization by providing data-driven answers to "which approach works better?" Without testing, teams rely on opinions and assumptions that are often wrong—what seems obviously better frequently underperforms.
Continuous testing compounds gains over time. A series of 5% improvements from systematic testing can double conversion rates within a year.
Examples in Practice
An e-commerce site tests checkout button copy, finding "Complete Purchase" outperforms "Buy Now" by 12% with high statistical confidence.
A landing page test reveals a longer form with qualifying questions generates fewer but higher-quality leads, improving ROI despite lower volume.
A media company tests thumbnail images systematically, developing data-backed creative guidelines that improve click-through rates site-wide.