As useful as the nonparametric statistics are, they are fairly blunt tools and can easily overlook real differences. Also, they can only tell us that “something is happening,” but cannot describe it in any precise detail.
Parametric statistics assume more about the quality of the data, but in return they can tell us more about what is going on with those data. The most common parametric statistics assume the “General Linear Model”—that is, they assume that the “true,” underlying distribution of the data can be described by a straight line (or one of its variants). We will look particularly at correlation and analysis of variance.
© 1996 A.J.Filipovitch
Revised 11 March 2005