For each uncertain variable in a simulation, you define the possible values with a probability distribution. The type of distribution you select depends on the conditions surrounding the variable. For example, some common distribution types are shown in Figure 74, Common Distribution Types: normal, triangular, uniform, and lognormal
During a simulation, the value to use for each variable is selected randomly from the defined possibilities.
A simulation calculates numerous scenarios of a model by repeatedly picking values from the probability distribution for the uncertain variables and using those values for the cell. Commonly, a Crystal Ball simulation calculates hundreds or thousands of scenarios in just a few seconds. The following section, A Probability Example, shows how a probability distribution relates to a simple set of employment data.
Crystal Ball works with two types of distributions, described in Continuous and Discrete Probability Distributions. For suggestions about using the best distribution when defining an assumption, see Selecting Probability Distributions. Probability Distribution Descriptions describes the properties and uses of each distribution available in Crystal Ball.