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**.*

- General Description
- Definitions & Mathematical Basis
- Sources of Data & Calculation Process
- Interpreting the Results
- Cases for Study
- Bibliography

© 1996 A.J.Filipovitch

Revised 11 March 2005