What is A/B testing?
A/B testing is an experimental method that compares two versions of a design element by randomly assigning users to each version and measuring which performs better on a predefined metric.
How it works
In a typical A/B test, traffic is split between a control (version A) and a variant (version B). The test runs until a statistically significant difference is detected or a predetermined sample size is reached. Key considerations include choosing the right metric (conversion rate, click-through rate, retention), ensuring adequate sample size to detect meaningful effects, and avoiding peeking at results before the test is complete. A/B testing provides causal evidence that a design change caused an outcome difference, unlike observational analytics.
Applied example
An e-commerce site tests two checkout button designs: the current gray button (A) versus a green button (B). After 10,000 visitors per variant, the green button shows a 3.2% higher conversion rate with p
Why it matters
A/B testing brings experimental rigor to design decisions, replacing opinion-based debates with evidence and enabling teams to iteratively optimize experiences based on actual user behavior.
