The Monte Carlo simulation has become a popular method for dealing with uncertainty within analyses. The popularity of the Monte Carlo method is due in some measure to the increased computing power available to reconstructionists. Plug-in modules to spreadsheet packages also enable easy access to Monte Carlo simulations. However, the presence of such software does not completely automate the process, and the reconstructionist must still exercise judgment in applying the Monte Carlo method to accident reconstruction. Two areas requiring judgment are examined in this paper. First, one of the key factors underlying the Monte Carlo analysis is the assumed probability distribution of the individual factors within the analysis. The literature has examples and recommendations for assuming normal, uniform, or custom distributions for input parameters. The paper examines what effect the assumed input distributions have on the resulting probability distributions. Second, with the large number of samples typically considered during a Monte Carlo analysis, the resulting probability distribution tends to be normal and lends itself well to statistical interpretation as to the “most likely” range of the desired parameter. The analysis in this paper was performed with a plug-in to Excel called Crystal Ball®, and the version of Crystal Ball® used for this paper allows the user to selectively filter results which do not agree with physical reality (for example, non-equal force balances within a crush energy analysis). When filtering results, the final probability distribution can be skewed. Therefore, this paper also examines methods of identifying the “most likely” range from within a skewed probability distribution.