If two linear models have different sets of explanatory variables and the same variable to be explained, the residual variance of the correct model (S^2"n) has a smaller mean value than that of the incorrect one (t^2"n). This note shows under fairly general conditions that S^2"n < t^2"n will hold with probability arbitrarily close to 1 provided that the sample size n is large enough.