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Concept
A/B testing
Summary
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.
"A/B testing" is a shorthand for a simple randomized controlled experiment, in which a number of samples (e.g. A and B) of a single vector-variable are compared. These values are similar except for one variation which might affect a user's behavior. A/B tests are widely considered the simplest form of controlled experiment, especially when they only involve two variants. However, by adding more variants to the test, its complexity grows.
A/B tests are useful for understanding user engagement and satisfaction of online features like a new feature or product. Large social media sites like LinkedIn, Facebook, and Instagram use A/B testing to make user experiences more successful and as a way to streamline their services.
Today, A/B tests are being used also for conducting complex experiments on subjects such as network effects when users are offline, how online services affect user actions, and how users influence one another. A/B testing is used by data engineers, marketers, designers, software engineers, and entrepreneurs, among others. Many positions rely on the data from A/B tests, as they allow companies to understand growth, increase revenue, and optimize customer satisfaction.
Version A might be used at present (thus forming the control group), while version B is modified in some respect vs. A (thus forming the treatment group).
Official source
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