Models that predict the likelihood of an event can be difficult to interpret. If the model missed an event, does that mean it was a bad model? Or were the results simply within the confidence bounds of the model? Using just one value can lead to erroneous conclusions. To try to give a better result, we developed a process to simulate the likelihood of an event occurring at a particular time and use those results to understand how we judge the model. This process is built to run using JMP and JSL to develop a user interface for inputting data and a model, run the simulation and visually provide the results to give a quick check of the validity of the model. For large data sets, this can be a particularly tedious and computationally onerous task. JMP provides the capability to store a matrix of data into a cell, and this feature is used to improve the running speed of the simulation. This presentation will walk through the overall process of validating the model, along with the JSL scripting of building the tool and interpreting the output.