Russel-Research-Method-in-Anthropology

640 Chapter 20
What to do with outliers? Try using the median to describe the central tendency
rather than eliminating cases. Or report the results of your analysis with
and without outliers, as I just did with figures 20.9a and b.
Tests of Significance
This is a hot topic in social science. Some years ago, researchers began
arguing that statistical tests of significance are virtually useless (Rozeboom
1960; Labovitz 1971b; Carver 1978), and the drumbeat has continued ever
since (Carver 1993; Cohen 1994).
I wouldn’t go that far. Consider the hypothesis that the universe is expanding.
As Wainer (1999) points out, being able to reject the null hypothesis at
p .05 would be quite a contribution. Will Dr. X be denied tenure a year from
now? Lots of people, says Wainer, would be happy to know that they could
reject the null hypothesis at, say, p .001.
It’s true that if you don’t have a representative sample, then a test of statistical
significance doesn’t allow you to generalize beyond your particular sample
of data. On the other hand, if you get significant results on a nonrandom sample,
at least you can rule out the operation of random properties in your sample
(Blalock 1979:239ff).
Use tests of significance but remember that they aren’t magical and that the
.01 and .05 levels of significance, while widely accepted tribal customs, are
not sacred. They are simply conventions that have developed for convenience
over the years. Greenwald et al. (1996:181–182) offer some useful advice
about reporting p values.
1. In many situations, it’s enough to use simple asterisks in your prose to indicate
statistical significance. But if you need to report p values, then do so with an
sign, not with a or sign. If a p value is .042, don’t report it as p .05 (‘‘the
probability is less than .05’’). Just report it as p .042 and be done with it.
Probability, just like the confidence we have in probabilistic results, is a continuous
variable. Why cut out all that information with arbitrary cutoffs?
2. A single p value of .05 is an indicator of a relation, but is not convincing support
for a hypothesis. By tradition, researchers almost never report probabilities that
are greater than .05. Five repeated results of p .06, or even .10, are more
convincing than a single result of p .05 that something’s really going on.
The Bonferroni Correction
If you want to be especially cautious in reporting correlations, you can
apply a test known as the Bonferroni correction. There are two kinds of spu-