The problem with speaking about the average error of a given statistical model is that it is difficult to determine how much of the error is due to the model and how much is due to randomness. The mean square error (MSE) provides a statistic that allows for researchers to make such claims. MSE simply refers to the mean of the squared difference between the predicted parameter and the observed parameter. Formally, this can be denned as In Equation (1), E represents the expected value of the squared difference between an estimate of an unknown parameter (θ∗) and the actual observed value (θ) of the parameter. In this instance, the expected value of the MSE simply refers to the average error one would expect given the parameter estimate. MSE is often categorized as a “loss function,” meaning that it represents how wrong the estimated parameter actually is, allowing one Substantively, ...