The accuracy of the simulation is primarily governed by two factors:
Generally speaking, the more trials you run in a simulation, the greater the accuracy of the statistics and percentiles information. This greater accuracy comes at the expense of lengthier simulation times and higher memory usages (see later sections on simulation speed and memory usage). Also, for a given number of trials, the accuracy of the statistics and percentiles greatly depends on the shape and nature of the forecast distribution.
If you are not sure how many trials to run for a specific level of accuracy, you can use the precision control feature of Crystal Ball to automatically determine the appropriate number of trials to run. For a detailed picture of a simulation’s statistical accuracy, you can run the Bootstrap tool to generate a forecast chart for each statistic or percentile of interest.
The sampling method is the other primary factor governing simulation accuracy. During a simulation, Monte Carlo sampling generates natural, "what-if" type scenarios while Latin Hypercube’s sampling is constrained, but more accurate. See Sampling Method for further discussion on sampling methods.