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Part II: Number-Crunching Basics
Summarizing data has other purposes, as well. After all the data have been
collected from a survey or some other kind of study, the next step is for
the researcher to try to make sense out of the data. Typically, the first step
researchers take is to run some basic statistics on the data to get a rough
idea about what’s happening in it. Later in the process, researchers can do
more analyses to formulate or test claims made about the population the
data came from, estimate certain characteristics about the population (like
the mean), look for links between variables they measured, and so on.
Another big part of research is reporting the results, not only to your peers, but
also to the media and the general public. Although a researcher’s peers may
be anxiously waiting to hear about all the complex analyses that were done on
a data set, the general public is neither ready for nor interested in that. What
does the public want? Basic information. Statistics that make a point clearly and
concisely are usually used to relay information to the media and to the public.
If you really need to learn more from data, a quick statistical overview isn’t
enough. In the statistical world, less is not more, and sometimes the real story
behind the data can get lost in the shuffle. To be an informed consumer of sta-
tistics, you need to think about which statistics are being reported, what these
statistics really mean, and what information is missing. This chapter focuses
on these issues.
Crunching Categorical Data:
Tables and Percents
Categorical data (also known as qualitative data) capture qualities or charac-
teristics about the individual, such as a person’s eye color, gender, political
party, or opinion on some issue (using categories such as Agree, Disagree, or
No opinion). Categorical data tend to fall into groups or categories pretty nat-
urally. “Political party,” for example, typically has four groups in the United
States: Democrat, Republican, Independent, and Other. Categorical data often
come from survey data, but they can also be collected in experiments. For
example, in an experimental test of a new medical treatment, researchers
may use three categories to assess the outcome of the experiment: Did the
patient get better, worse, or stay the same while undergoing the treatment?
Categorical data are often summarized by reporting the percentage of indi-
viduals falling into each category. For example, pollsters may report politi-
cal affiliation statistics by giving the percentage of Republicans, Democrats,
Independents, and Others. To calculate the percentage of individuals in a certain
category, find the number of individuals in that category, divide by the total
number of people in the study, and then multiply by 100%. For example, if a

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