A/B testing is an experimental approach to html"}]' tabindex='0' role='link'>user experience design, which aims to identify changes to web pages that increase or maximize an outcome of interest / KPI (e.g., click-through rate for a banner advertisement). As the name implies, two versions (A and B) are compared, which are identical except for one variation that might impact a user’s behavior. Version A might be the currently used version, while Version B is modified in some respect.
A/B testing (also known as bucket testing, split-run testing, or split testing) is a user experience research methodology. A/B tests consist of a randomized experiment that usually involves two variants (A and B), although the concept can be also extended to multiple variants of the same variable. It includes application of statistical hypothesis testing or "two-sample hypothesis testing" as used in the field of statistics. A/B testing is a way to compare multiple versions of a single variable, for example by testing a subject's response to variant A against variant B, and determining which of the variants is more effective.
