Page 286 - Statistics for Dummies

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Part V: Statistical Studies and the Hunt for a Meaningful Relationship
What impact would this selective volunteering have on the results of the study?
If only the health nuts (who probably already have excellent heart rates) vol-
unteer to be in the treatment group, the researcher will be looking only at the
effect of the treatment (running five miles) on very healthy and active people.
He won’t see the effect that running five miles has on the heart rates of couch
potatoes. This non-random assignment of subjects to the treatment and control
groups could have a huge impact on the conclusions he draws from this study.
To avoid major bias in the results of an experiment, subjects must be ran-
domly assigned to treatments by a third party and not be allowed to choose
which group they will be in. The goal of random assignment is to create
homogenous groups; any unusual characteristics or biases have an equal
chance of appearing in any of the groups. Keep this in mind when you evaluate
the results of an experiment.
Controlling for confounding variables
Suppose you’re participating in a research study that looks at factors influenc-
ing whether you catch a cold. If a researcher records only whether you got
a cold after a certain period of time and asks questions about your behavior
(how many times per day you washed your hands, how many hours of sleep
you get each night, and so on), the researcher is conducting an observational
study. The problem with this type of observational study is that without con-
trolling for other factors that may have had an influence and without regulat-
ing which action you were taking when, the researcher won’t be able to single
out exactly which of your actions (if any) actually impacted the outcome.
The biggest limitation of observational studies is that they can’t really show
true cause-and-effect relationships, due to what statisticians call confounding
variables. A confounding variable is a variable or factor that was not controlled
for in the study but can have an influence on the results.
For example, one news headline boasted, “Study links older mothers, long
life.” The opening paragraph said that women who have a first baby after age
40 have a much better chance of living to be 100, compared to women who
have a first baby at an earlier age. When you get into the details of the study
(done in 1996) you find out, first of all, that it was based on 78 women in sub-
urban Boston who were born in 1896 and had lived to be at least 100, com-
pared to 54 women who were also born in 1896 but died in 1969 (the earliest
year the researchers could get computerized death records). This so-called
“control group” lived to be exactly 73, no more and no less. Of the women who
lived to be at least 100 years of age, 19% had given birth after age 40, whereas
only 5.5% of the women who died at age 73 had given birth after age 40.

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