Use the beta distribution for random variables between 0 and 1. The beta distribution is frequently used to model the distribution of order statistics—for example, the k^{th} order statistic from a sample of n uniform (0, 1) variables has a beta (k, n + 1 – k) distribution—and to model events that are defined by minimum and maximum values. The beta distribution is often rescaled to model the time to completion of a task. The beta distribution is also used in Bayesian statistics, for example, as the prior distribution of a binomial probability.
The beta distribution is a continuous distribution defined by two shape parameters. The distribution can take on different shapes depending on the values of the two parameters.
When both shapes equal 1, the beta distribution is the uniform distribution.
When both shapes are less than 1, the distribution is U-shaped.
When both shapes are equal and greater than 1, the distribution is symmetric.
When the first shape is greater than the second shape, the distribution is skewed to the left.
When the first shape is less than second shape, the distribution is skewed to the right.