SAS supports many different distributions in various functions and procedures. Before statistical analysis, you must know your data well and be certain about the distribution of your data. Therefore, I have written introductions to the most common distributions. First, I give a small theoretical presentation of the distribution and its Probability Density Function, PDF (Probability Mass Function, PMF in the discrete cases). Then a graphical representation of both the Probability Density Function and its corresponding Cummulative Density function, CDF, along with the SAS code creating these. Following will be a few lines of how to interpret the distribution and where we typically use it. Finally, I will list a small SAS code example, where you can edit and play around with the parameter(s) of the Probability Density Function and visually assess how it affects the shape of the distribution.
Continuous Distributions
Discrete Distributions
If you need a brush up on probability distributions in general, check out the videos Probability Density Functions for Continuous Random Variables and Constructing a probability distribution for random variable at Khan Academy.
For distribution fitting of both continuous and discrete probability distributions, consult the SAS documentation for PROC UNIVARIATE and PROC GENMOD. In addition, I have previously written blog posts about distribution fitting using these procedures in Fit Continuous Distribution in SAS and Fit Discrete Distribution in SAS.