This type of probability plot is constructed as follows. First, within each category, the values (observations) are rank ordered. From these ranks, you can compute z values (i.e., standardized values of the normal distribution) based on the assumption that the data come from a normal distribution (see Computational Note). These z values are plotted on the Y-axis in the plot. If the observed values (plotted on the X-axis) are normally distributed, all values should fall onto a straight line. If the values are not normally distributed, they will deviate from the line. Outliers may also become evident in this plot. If there is a general lack of fit, and the data seem to form a clear pattern (e.g., an S shape) around the line, then the variable may have to be transformed in some way (e.g., a log transformation to "pull-in" the tail of the distribution, etc.) before some statistical techniques that are affected by non-normality can be used.

When you create categorized probability plots, a series of standard probability plots, one for each category of cases identified by the X or X and Y category variables (or identified by the multiple subset criteria) is produced.

As with 2D Normal Probability Plots, you can create normal, half-normal, and detrended normal probability plots.