 Statistical invariance principle

If sample transformations preserve the model and both hypotheses, the invariance principle restricts attention to tests whose decisions are unchanged by those transformations. Such tests are functions of a maximal invariant. In estimation, the analogous requirement is equivariance, accompanied by an invariant loss function so that risks transform compatibly. The principle respects symmetries; it does not by itself prove optimality among all unrestricted procedures.