This course is designed to provide you with an introduction to various statistical issues routinely encountered in psychological studies, especially experiments. We will focus on typical analyses used in experimental designs (especially t-tests and Analysis of Variance [ANOVA]). Along with a focus on the concepts underlying the statistics, we will integrate practical issues associated with using prevalent statistical packages to conduct the analyses.

By the end of the course, students should understand the conceptual underpinnings of hypothesis tests for between-subject and within-subject designs, including ANOVA results for between-subject, within-subject, and, if time, mixed designs. They should be able to follow-up those basic analyses with different types of analyses used to help in understanding what the omnibus test statistics mean (e.g., post-hoc tests).

Students should also be able to decide when each type of analysis is appropriate or not appropriate and use common statistical software to take raw data and carry the appropriate analysis through to the research conclusions. In many weeks, we will also provide a set of made-up data or real data (on Carmen) with features that cannot be seen with standard stat tests. The aim is to have students explore data by plotting and exploring them in other ways, think of these like puzzles and challenges. Breakthroughs in research often come from seeing something new in experimental results.
Prereq: 1100 or 1100H, and a grade of B or above in 2220 or 2220H.