Simulation output clearly depends on the form of the input distributions used to drive the model. Often these input distributions are fitted using finite samples of real-world data. The finiteness of the samples introduces errors in the input distributions, affecting the output. Yet this propagation of input model uncertainty to output uncertainty is rarely considered in simulation output analysis. This tutorial presents a discussion of input uncertainty issues and recently developed methodological approaches, set in the context of input uncertainty methods proposed over the past twenty years.