Charles M. Stein (born March 22, 1920), an American mathematical statistician, is emeritus professor of statistics at Stanford University. He received his Ph.D in 1947 at Columbia University with advisor Abraham Wald. He is known for Stein's paradox in decision theory, which shows that ordinary least squares estimates can be uniformly improved when many parameters are estimated; for Stein's lemma, giving a formula for the covariance of one random variable with the value of a function of another when the two random variables are jointly normally distributed; and for Stein's method, a way of proving theorems such as the Central Limit Theorem. He is a member of the National Academy of Sciences.