Page 290 - Statistics for Dummies

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Part V: Statistical Studies and the Hunt for a Meaningful Relationship
✓ The data are unbiased — they contain no systematic errors that either
add to or subtract from the true values. Biased data are data that sys-
tematically overmeasure or undermeasure the true result. Bias can occur
almost anywhere during the design or implementation of a study. Bias
can be caused by a bad measurement instrument (like that bathroom
scale that’s “always” 5 pounds over), by survey questions that lead par-
ticipants in a certain way, or by researchers who know what treatment
each subject received and who have preconceived expectations.
Bias is probably the number-one problem in collecting good data. However,
you can minimize bias with methods similar to those discussed in Chapter 16
for surveys and in the “Making random assignments” section earlier in this
chapter, and by making your experiments double-blind whenever possible.
Double-blind means neither the subjects nor the researchers know who got
what treatment or who is in the control group. The subjects need to be oblivi-
ous to which treatment they’re getting so that the researchers can measure the
placebo effect. And researchers should be kept in the dark so they don’t treat
subjects differently by either expecting or not expecting certain responses
from certain groups. For example, if a researcher knows you’re in the treat-
ment group to study the side effects of a new drug, she may expect you to get
sick and therefore may pay more attention to you than if she knew you were in
the control group. This can result in biased data and misleading results.
If the researcher knows who got what treatment but the subjects don’t know,
the study is called a blind study (rather than a double-blind study). Blind stud-
ies are better than nothing, but double-blind studies are best. In case you’re
wondering: In a double-blind study, does anyone know which treatment was
given to which subjects? Relax; typically a third party, such as a lab assistant,
does that part.
In some cases the subjects know which group they’re in because it’s
unconcealable — for example, when comparing the benefits of doing yoga
versus jogging. However, bias can be reduced by not telling the subjects the
precise purpose of the study. This irregular type of plan would have to be
reviewed by an institutional review board to make sure it isn’t unethical to
do; see the earlier section “Respecting ethical issues.”
Analyzing the data properly
After the data have been collected, they’re put into that mysterious box
called the statistical analysis for number crunching. The choice of analysis is
just as important (in terms of the quality of the results) as any other aspect
of a study. A proper analysis should be planned in advance, during the design
phase of the experiment. That way, after the data are collected, you won’t
run into any major problems during the analysis.

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