It is quite common to see experimental data analysed according to a variety of models of ligand-receptor interaction. Often, parameters derived from such models are compared statistically. The most commonly employed statistical analyses contain explicit assumptions about the underlying distributions of the model parameters being compared, yet the validity of these assumptions is not often ascertained. In this article, Arthur Christopoulos describes a general approach to Monte Carlo simulation of data, and outlines how the analysis of such simulated data may be used to address the question of the distribution of model parameters. The results of such an exercise can guide the researcher to the appropriate choice of statistical test or data transform.