If you have historical data available, Crystal Ball’s distribution fitting feature can substantially simplify the process of selecting a probability distribution when creating assumptions. Not only is the process simplified, but the resulting distribution more accurately reflects the nature of the data than if the shape and parameters of the distribution were estimated.
Distribution fitting automatically matches historical data against probability distributions. A mathematical fit determines the set of parameters for each distribution that best describe the characteristics of the data. Then, the closeness of each fit is judged using one of several standard goodness-of-fit tests. The highest ranking fit is chosen to represent the data. You can select from among all distributions supported by Crystal Ball except the yes-no distribution.
For instructions and additional information, see Using Distribution Fitting for Assumptions.