Page 29 - Statistics for Dummies

Chapter 1: Statistics in a Nutshell
Selecting a good sample
Statisticians have a saying, “Garbage in equals garbage out.” If you select your
subjects (the individuals who will participate in your study) in a way that is
biased — that is, favoring certain individuals or groups of individuals — then
your results will also be biased. It’s that simple.
Suppose Bob wants to know the opinions of people in your city regarding a
proposed casino. Bob goes to the mall with his clipboard and asks people
who walk by to give their opinions. What’s wrong with that? Well, Bob is only
going to get the opinions of a) people who shop at that mall; b) on that par-
ticular day; c) at that particular time; d) and who take the time to respond.
Those circumstances are too restrictive — those folks don’t represent a cross
section of the city. Similarly, Bob could put up a Web site survey and ask
people to use it to vote. However, only people who know about the site, have
Internet access, and want to respond will give him data, and typically only
those with strong opinions will go to such trouble. In the end, all Bob has is a 13
bunch of biased data on individuals that don’t represent the city at all.
To minimize bias in a survey, the key word is random. You need to select your
sample of individuals randomly — that is, with some type of “draw names out
of a hat” process. Scientists use a variety of methods to select individuals at
random, and you see how they do it in Chapter 16.
Note that in designing an experiment, collecting a random sample of people
and asking them to participate often isn’t ethical because experiments impose
a treatment on the subjects. What you do is send out requests for volunteers
to come to you. Then you make sure the volunteers you select from the group
represent the population of interest and that the data is well collected on those
individuals so the results can be projected to a larger group. You see how that’s
done in Chapter 17.
After going through Chapters 16 and 17, you’ll know how to dig down and
analyze others’ methods for selecting samples and even be able to design
a plan you can use to select a sample. In the end, you’ll know when to say
“Garbage in equals garbage out.”
Avoiding bias in your data
Bias is the systematic favoritism of certain individuals or certain responses.
Bias is the nemesis of statisticians, and they do everything they can to mini-
mize it. Want an example of bias? Say you’re conducting a phone survey

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