Two-sample testing

Two-sample testing is a test performed on independent random samples from two different populations in hypothesis testing, aiming to determine whether the difference between these two populations is statistically significant. This testing method has important application value in the field of machine learning, such as domain adaptation, covariate shift, and generative adversarial networks.