Page 279 - Statistics for Dummies

Chapter 17: Experiments: Medical Breakthroughs or Misleading Results?
✓ Treatment: A treatment is a combination of the levels of the factors
being studied. If you only have one factor, the levels and the treatments
are the same thing. If you have more than one factor, each combination
of levels of the factors is called a treatment.
For example, if you want to study the effects of the type of weight loss

program and the amount of water consumed daily, you have two fac-
tors: 1) the type of program, with 3 levels (Weight Watchers, South
Beach, Potato Diet); and 2) the amount of water consumed, with, say,
3 levels (24, 48, and 64 ounces per day). In this case, there are 3 ∗ 3 = 9
treatments: Weight Watchers and 24 ounces of water per day; Weight
Watchers and 48 ounces of water per day, . . . all the way up to the
famous Potato Diet and 64 ounces of water per day. Each subject is
assigned to one treatment. (With my luck, I’d get that last treatment.)

✓ Cause and effect: A factor and a response have a cause-and-effect
relationship if a change in the factor results in a direct change in the
response (for example, increasing calorie intake causes weight gain).
In the following sections, you see the differences between observational 263
studies and experiments, when each is used, and what their strengths and/or
weaknesses may be.
Observing observational studies
Just like with tools, you want to find the right type of study for the right job.
In certain situations, observational studies are the optimal way to go. The
most common observational studies are polls and surveys (see Chapter 16).
When the goal is simply to find out what people think and to collect some
demographic information (such as gender, age, income, and so on), surveys
and polls can’t be beat, as long as they’re designed and conducted correctly.
In other situations, especially those looking for cause-and-effect relationships,
observational studies aren’t optimal. For example, suppose you took a couple
of vitamin C pills last week; is that what helped you avoid getting that cold
that’s going around the office? Maybe the extra sleep you got recently or
the extra hand-washing you’ve been doing helped you ward off the cold. Or
maybe you just got lucky this time. With so many variables in the mix, how
can you tell which one had an influence on the outcome of your not getting a
cold? An experiment that takes these other variables into account is the way
to go.
When looking at the results of any study, first determine what the purpose of
the study was and whether the type of study fits the purpose. For example,
if an observational study was done instead of an experiment to establish a
cause-and-effect relationship, any conclusions that are drawn should be care-
fully scrutinized.

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