Also known as: A and B Testing Β· A B Testing
A/B Testing is a controlled statistical comparison of two or more variants of a system, model, interface, or process. It measures whether one variant performs better on selected metrics and whether the observed difference is likely to be meaningful rather than random.
A statistical way of comparing two (or more) techniques---the A and the B . Typically, the A is an existing technique, and the B is a new technique. A/B testing not only determines which technique performs better but also whether the difference is statistically significant. A/B testing usually compares a single metric on two techniques; for example, how does model accuracy compare for two techniques? However, A/B testing can also compare any finite number of metrics.