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.
There are several reasons why we want to conduct statistical analyses when looking at our program evaluation data.
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.
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.
Correct and accepted statistical methods are usually required to get results published in academic journals or proceedings from professional meetings.
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.
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.