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Chapter 1: Beyond Number Crunching: The Art and Science of Data Analysis
and listed in pull-down menus. Now almost anyone (even me) can quickly see
how to find the necessary procedure and tell the computer what to do. The
results come instantly and successfully, and you can cut and paste them into
a word-processing document without blinking an eye. For example, compar-
ing the weight loss for people on different weight-loss programs now takes
less than three clicks of the mouse to perform, which is great news for folks
like me.
Many very useful and efficient statistical software packages exist, including
SAS, SPSS, Data Desk, Stat Crunch, MS Excel, and Minitab, and each one has
its own pros and cons (and its own users and protesters). My software of
choice, and the one I reference throughout this book, is Minitab, because it’s
very easy to use, the results are correct, the output is very clear and profes-
sional looking, and the software’s loaded with all the data-analysis techniques
that are used in intermediate statistics as well as in this book. While a site
license for Minitab can be expensive, the downloadable student version is
available for rent for only a few bucks a semester.
The downside of today’s 11
statistical software
You may be wondering where the downside is in all of this. Is it too good to
be true that what was once a tedious, complicated process for analyzing data
has now become as easy as checking your e-mail on your cell phone? Yes and
no. Yes, it’s too good to be true that the software practically does everything
for you — if you don’t pay attention to what the programs are really doing.
Yes, it’s too good to be true if you don’t understand that conditions need to
be checked in every situation before an analysis should be applied. Yes, it’s
too good to be true if you take all the results as complete and utter gospel
(as too many statistician wannabees do).
Bottom line: Today’s software packages are too good to be true if you don’t
have a clear and thorough understanding of the intermediate level of statis-
tics that lie underneath them.
Here’s the good news, though. By reading this book, you gain the understand-
ing you need to set you up for success. You get enough of the underlying
intermediate statistical concepts to be empowered, but not be dangerous.
You find out what conditions need to be checked on the data before applying
an analysis and how to check them. You get a good feel for which analyses to
use to answer your question (and which ones can cause you trouble), and
you become aware of the kinds of results you can expect. Most importantly,
you discover what’s possible and appropriate to conclude from your analysis
and what limitations and caveats you need to make.