If the model is correctly specified, it can be shown under mild assumptions that the expected value of the MSE for the training set is ("n" − "p" − 1)/("n" + "p" + 1) < 1 times the expected value of the MSE for the validation set (the expected value is taken over the distribution of training sets). Thus if we fit the model and compute the MSE on the training set, we will get an optimistically biased assessment of how well the model will fit an independent data set. This biased estimate is called the "in-sample" estimate of the fit, whereas the cross-validation estimate is an "out-of-sample" estimate.