The process of validating a stochastic simulation model involves the comparison of data generated by the model with corresponding data from the real system. Instead of applying statistical tests to determine whether the model adequately represents the real system, an alternate approach is to estimate the error that will result when the model is used to draw inferences about the real system. Regression methodology is proposed for estimating this error as a function of the levels of the input variables of the model. Confidence intervals for expected error and prediction intervals for actual error are given. An example of estimating the error in the volume predictions of a stochastic forest stand simulator is given.