Page 51 - Intermediate Statistics for Dummies

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Part I: Data Analysis and Model-Building Basics
The trouble with these requirements is that many times people forget or just
don’t bother to check those conditions, and if the conditions are actually not
met, the entire analysis goes out the window, and the researcher doesn’t
even know it. Or, someone finds out that the conditions aren’t being met, yet
she still goes ahead and uses the procedures anyway (for more on this faux
pas, see the section in this chapter “No [data] fishing allowed”).
While many of the traditional methods are what statisticians call robust, with
respect to violations of their conditions (that’s fancy terminology for the fact
that they’re pretty forgiving), you can only push the window so far. Proceeding
to use a statistical procedure that isn’t appropriate causes a great deal of trou-
ble with respect to the correctness of the conclusions and the credibility of the
researcher.
Have no fear, nonparametrics comes to your rescue. If the conditions aren’t
met for a data-analysis procedure that you want to do, chances are that an
equivalent nonparametric procedure is waiting in the wings. And the good
news is that they’re generally pretty tame, in terms of formulas, and most sta-
tistical software packages can do them just as easily as the regular (paramet-
ric) procedures.
Conditions aren’t checked automatically by statistical software packages,
before doing a data analysis. It’s up to the user to check any and all appropri-
ate conditions, and if they’re seriously violated, to take another course of
action. Many times a nonparametric procedure is just the ticket. For much
more information on different nonparametric procedures, see Chapters 16
through 19.