Steinâ€™s paradox Steinâ€™s example, perhaps better known under the name Steinâ€™s Paradox, is a well-known example in statistics that demonstrates the use of shrinkage to reduce the mean squared error (\(L_2\)-risk) of a multivariate estimator with respect to classical (unbiased) estimators, such as the maximum likelihood estimator.