The Cramer-Rao Bound is a fundamental concept in statistics that provides a lower limit on the variance of unbiased estimators. It states that no unbiased estimator can have a variance smaller than the inverse of the Fisher information, which measures the amount of information that an observable random variable carries about an unknown parameter. This bound is crucial in the field of estimation theory, as it helps researchers understand the efficiency of different estimators. If an estimator reaches the Cramer-Rao Bound, it is considered efficient, meaning it achieves the lowest possible variance among all unbiased estimators for that parameter.