The Manhattan Project needed a repeatable mathematical model to solve complex differential equations that could not be solved by conventional deterministic mathematical techniques. Monte Carlo Simulation gave them a viable probabilistic technique. The fundamental principle behind Monte Carlo Simulation is that if we can describe each input variable to a system or scenario by a probability distribution then we can model likely outcome of several independent or dependent variables acting together. All variables inherently have uncertain future values, otherwise we would call them Constants. We can describe range of potential values by means of an appropriate probability distribution, of which there are scores, if not hundreds of different ones. One common mistake made by estimators and other number jugglers new to Monte Carlo Simulation is that they tend to use too small a sample size, i.e. too few iterations. The natural tendency for positive skewness in input variables of time and cost is a valid consideration at lowest detail level.