Page 340 - Statistics for Dummies

324
Part VI: The Part of Tens
✓ Researchers aren’t always objective. Suppose in a drug study one group
of patients is given a sugar pill and the other group is given the real
drug. If the researchers know who received the real drug, they may inad-
vertently pay more attention to those patients to see if it’s working; they
may even project results onto the patients (such as saying, “I bet you’re
feeling better, aren’t you?”). This creates a bias in favor of the drug. (See
Chapter 17 for more information on setting up good experiments.)
To spot biased data, examine how the data were collected. Ask questions
about the selection of the participants, how the study was conducted, what
questions were used, what treatments (medications, procedures, therapy,
and so on) were given (if any) and who knew about them, what measurement
instruments were used and how they were calibrated, and so on. Look for sys-
tematic errors or favoritism, and if you see too much of it, ignore the results.
Search for a Margin of Error
The word error has a somewhat negative connotation, as if an error is some-
thing that is always avoidable. In statistics, that’s not always the case. For
example, a certain amount of what statisticians call sampling error will always
occur whenever someone tries to estimate a population value using anything
other than the entire population. Just the act of selecting a sample from the
population means you leave out certain individuals, and that means you’re
not going to get the precise, exact population value. No worries, though.
Remember that statistics means never having to say you’re certain — you
have to only get close. And if the sample is large enough, the sampling error
will be small (assuming it’s good data of course).
To evaluate a statistical result, you need a measure of its accuracy — typi-
cally through the margin of error. The margin of error tells you how much
the researcher expects her results to vary from sample to sample. (For more
information on margin of error, see Chapter 12.) When a researcher or the
media fail to report the margin of error, you’re left to wonder about the accu-
racy of the results, or worse, you just assume that everything is fine, when in
many cases, it’s not.
When looking at statistical results in which a number is being estimated (for
example, the percentage of all Americans who think the president is doing a
good job), always check for the margin of error. If it’s not included, ask for it!
(Or if given enough other pertinent information, you can calculate the margin
of error yourself using the formulas in Chapter 13.)

3/25/11 8:12 PM
29_9780470911082-ch20.indd 324 3/25/11 8:12 PM
29_9780470911082-ch20.indd 324