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Statistics is a general term. Every time
you report a mean, median, or standard deviation of your data, you are
reporting a statistic. Specifically, this type of statistics are
called *descriptive statistics*. We can also use statistical
tests and report the statistics from these tests, such as *p*-values
or *r-squared* values. In order to convey the implications
of these statistics and the underlying data, we might present them in
a plot or table.

### The importance of statistics in program evaluation

There are several reasons why we want to conduct statistical analyses when looking at our program evaluation data.

Communication

Descriptive statistics and statistical tests can
be used to communicate with our colleagues. Once we understand
statistical tests—as well common statistics such as *p*-values,
confidence intervals, and *r-squared* values—we can communicate
our results in a shared language. A single well designed plot
with appropriate statistics is an effective tool to convey our findings.

Communication is a two-way street. We also need to be able to understand others when they share their results with us, whether it’s in a departmental presentation, a presentation at a professional meeting, in extension literature, or in a peer-reviewed journal article.

Adding rigor

Sometimes educators will simply report the change
in median scores before and after a course, or report a best fit line
for bivariate data. However, without applying a statistical tests
and reporting *p*-values or other appropriate statistics, it is
not clear to the reader (or probably the author!) if the reported effect
is real.

Getting things right

In order for your statistical analyses to provide correct results, you need to understand when they are appropriate and what the results mean. Also you will want to understand what tests and techniques are available in order to best be able to understand and explain your results.

Presenting results well

Knowledge of statistical tests and plots can inform the best ways to summarize and present results from your data.

Academic recognition

Correct and accepted statistical methods are usually required to get results published in academic journals or proceedings from professional meetings.

Program evaluation

Appropriate statistics help to evaluate programs, for example determining if there was an increase in student knowledge scores was statistically significant or if one teaching technique is better than another. Such information can guide future programming efforts.

### Applicability of statistical tests to other fields and situations

The statistical techniques presented in this book are applicable to a wide variety of disciplines, and are some of the most common used across fields. While the analyses presented in this book are common and relatively simple, understanding of these techniques will serve as a basis for more advanced analyses.