In this video, the question of finding the lower bound on the performance of all estimators for a particular probabilistic problem, as a benchmark with which to compare the performance of a given estimator. The Cramer-Rao lower bound (CRLB) is introduced in this video for this benchmark, for the class of unbiased estimators. The Fisher Information is discussed, and it is shown how to test for the existence of a minimum variance unbiased estimator (MVUE) which attains the CRLB. An example is shown for deriving the sample mean, which is a MVUE (and as a result also the mean square error (MSE) estimator). In the example, the minimum variance is found through the two alternate but equivalent expressions for the CRLB.