Page 287 - Statistics for Dummies

Chapter 17: Experiments: Medical Breakthroughs or Misleading Results?
I have a real problem with these conclusions. What about the fact that the
“control group” was based only on mothers who died in 1969 at age 73? What
about all the other mothers who died before age 73, or who died between the
ages of 73 and 100? What about other variables that may affect both mothers’
ages at the births of their children and longer life spans — variables such
as financial status, marital stability, or other socioeconomic factors? The
women in this study were in their thirties during the Depression; this may
have influenced both their life span and if or when they had children.
How do researchers handle confounding variables? They control for them as
best they can, for as many of them as they can anticipate, trying to minimize
their possible effect on the response. In experiments involving human sub-
jects, researchers have to battle many confounding variables.
For example, in a study trying to determine the effect of different types and
volumes of music on the amount of time grocery shoppers spend in the store
(yes, they do think about that), researchers have to anticipate as many pos-
sible confounding variables ahead of time and then control for them. What 271
other factors besides volume and type of music may influence the amount of
time you spend in a grocery store? I can think of several factors: gender, age,
time of day, whether you have children with you, how much money you have,
the day of the week, how clean and inviting the store is, how nice the employ-
ees are, and (most importantly) what your motive is — are you shopping for
the whole week, or are you just running in to grab a candy bar?
How can researchers begin to control for so many possible confounding fac-
tors? Some of them can be controlled for in the design of the study, such as
the time of the day, day of the week, and reason for shopping. But other fac-
tors (such as the perception of the store environment) depend totally on the
individual in the study. The ultimate form of control for those person-specific
confounding variables is to use pairs of people that are matched according
to important variables, or to just use the same person twice: once with the
treatment and once without. This type of experiment is called a matched-pairs
design. (See Chapter 15 for more on this.)
Before believing any medical headlines (or any headlines with statistics, for
that matter), look to see how the study was conducted. Observational studies
can’t control for confounding variables, so their results are not as statistically
meaningful (no matter what the statistics say) as the results of a well-designed
experiment are. In cases where an experiment can’t be done (after all, no one
can force you to have a baby after or before age 40), make sure the observa-
tional study is based on a large enough sample that represents a cross-section
of the population. And think about possible confounding variables that may
affect the conclusions being drawn.

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