Large sets of numbers can be daunting, and characterizing them in a few words or numbers can be even more daunting. This course considers how to take data sets--whether large or small--and describe them using a few numbers (descriptive statistics). This, however, is only a small portion of the course.
The majority of the course is dedicated to reaching statistically justified conclusions on the basis of these descriptive statistics. For instance, does the average value of one data set deviate from another in what might be called a "statistically significant" manner? To this end, the course covers cross tabulation of data (including the chi-square test), correlation, linear regression, Student's t-tests, analysis of variance (ANOVA), repeated measures analysis, and factor analysis.
Although the course does not always provide rigorous mathematical justifications for every aspect of the statistical tools and theory discussed therein, it does provide common-sense explanations of many of these aspects, and it includes numerous real-world examples to illustrate the use of these tools.
Thus, this course teaches students to take sets of data, describe them using a few numbers (including the mean, variance, and skewness), and then reach statistically justifiable conclusions about those data sets. Students should come away from the course with confidence in their ability to tackle basic applied statistics problems and with the fundamental knowledge needed to learn more in-depth statistical theory.