Page 291 - Statistics for Dummies

Chapter 17: Experiments: Medical Breakthroughs or Misleading Results?
Here’s the bottom line when selecting the proper analysis: Ask yourself the
question, “After the data are analyzed, will I be able to legitimately and correctly
answer the question that I set out to answer?” If the answer is “no,” then that
analysis isn’t appropriate.
Some basic types of statistical analyses include confidence intervals (used
when you’re trying to estimate a population value, or the difference between
two population values); hypothesis tests (used when you want to test a claim
about one or two populations, such as the claim that one drug is more effec-
tive than another); and correlation and regression analyses (used when you
want to show if and/or how one quantitative variable can predict or cause
changes in another quantitative variable). See Chapters 13, 15, and 18,
respectively, for more on each of these types of analyses.
When choosing how you’re going to analyze your data, you have to make sure
that the data and your analysis will be compatible. For example, if you want
to compare a treatment group to a control group in terms of the amount of
weight lost on a new (versus an existing) diet program, you need to collect 275
data on how much weight each person lost — not just each person’s weight at
the end of the study.
Making appropriate conclusions
In my opinion, the biggest mistakes researchers make when drawing conclu-
sions about their studies are the following (discussed in the following sections):
✓ Overstating their results
✓ Making connections or giving explanations that aren’t backed up by the
statistics
✓ Going beyond the scope of the study in terms of whom the results apply to
Overstating the results
Many times, the headlines in the media overstate actual research results.
When you read a headline or otherwise hear about a study, be sure to look
further to find out the details of how the study was done and exactly what
the conclusions were.
Press releases often overstate results, too. For example, in a recent press
release by the National Institute for Drug Abuse, the researchers claimed that
use of the street drug Ecstasy was down from the previous year. However,
when you look at the actual statistical results in the report, you find that
the percentage of teens from the sample who said they’d used Ecstasy was
lower than those from the previous year, but this difference was not found to
be statistically significant when they tried to project it onto the population

3/25/11 8:13 PM
25_9780470911082-ch17.indd 275
25_9780470911082-ch17.indd 275 3/25/11 8:13 PM