I ran across a model of linguistic clarity in describing a statistical test,
while browsing a web tutorial by Prof. Jeff Connor-Linton
<connorlj@georgetown.edu> at Department of Linguistics , Georgetown U.
His introduction to chi squared caught my eye, because it is enthusiastically
advocated on this list, from time to time.
"Chi square is a non-parametric test of statistical significance for
bivariate tabular analysis (also known as crossbreaks). Any appropriately
performed test of statistical significance lets you know the degree of
confidence you can have in accepting or rejecting an hypothesis. Typically,
the hypothesis tested with chi square is whether or not two different
samples (of people, texts, whatever) are different enough in some
characteristic or aspect of their behavior that we can generalize from our
samples that the populations from which our samples are drawn are also
different in the behavior or characteristic.
"A non-parametric test, like chi square, is a rough estimate of confidence;
it accepts weaker, less accurate data as input than parametric tests (like
t-tests and analysis of variance, for example) and therefore has less status
in the pantheon of statistical tests. "