Russel-Research-Method-in-Anthropology

642 Chapter 20
be comfortable with tests of significance at the p .10 level, especially if you
can repeat your finding in independent tests. Two or three repeated experiments
that produce similar results in the same direction at the .10 level of
significance are more convincing evidence of something going on than is one
experiment that just barely pans out at the .05 level.
On the other hand, you can always find significant covariations in your data
if you lower alpha (the level of significance) enough, so be careful. Remember,
you’re using statistics to get hints about things that are going on in your
data. I cannot repeat often enough the rule that real analysis (building explanations
and suggesting plausible mechanisms that make sense out of covariations)
is what you do after you do statistics.
I also can’t stress enough the difference between statistical significance
and practical significance. If you have a large enough n (in the thousands),
you will surely find significant statistical relations in your data. Each of those
relations may account for a small amount of the variance in what you’re interested
in understanding, but that doesn’t mean that the relations are of practical
use.
Lots of research across the world shows a strong correlation between
instruction about the dangers of unprotected sex and an increase in knowledge
about HIV/AIDS, but not necessarily any changes in risky sexual behavior.
Instruction in the use of contraceptives produces an increase in knowledge
about reproductive health, but not necessarily a decrease in fertility.
Even when an increase in knowledge does produce desired changes in
behavior, the practical significance of this information may be set aside by
political realities. If you start early enough, teaching children about the dangers
of smoking apparently produces a reduction in smoking. Suppose you
come up with a curriculum that costs $60,000 to implement in a school district
of 2,000 students and that produces an aggregate reduction of 10% in smoking
behavior and that the 10% is a statistically significant reduction. Will the
school board shell out this money? The statistical level of significance in the
results may play some role in the board’s decision, but I’ll bet that other things
will weigh even more heavily in their deliberations.
Statistical Power
The power of a statistical test is the probability of correctly accepting your
research hypothesis. If you’re thinking: ‘‘You mean it’s the probability of taking
‘yes’ for an answer?’’ then you’re right on track. As you know, the traditional
way to conduct research is to: (1) formulate a hypothesis; (2) turn the
hypothesis around into a null hypothesis; and then (3) try as hard as we can
to prove the null hypothesis.