Undergraduate statistics in psychology is an important, often challenging, course for students. The focus in psychology tends to be on hypothesis tests, such as t tests and analysis of variance. While adequate for some questions, there are many other topics we might include that could improve that data analytic abilities of students and improve psychological science in the long run. Topics such as generalized linear modeling, multilevel modeling, Bayesian statistics, model building and comparison, and causal analysis, could be introduced in an undergraduate psychological statistics course. For each topic, I discuss their importance and provide sources for instructor’s continuing education. These topics would give students greater flexibility in analyzing data, allow them to conduct more meaningful analyses, allow them to understand more modern data analytic approaches, and potentially help the field of psychology in the long run, by being one part of the strategy to address the reproducibility problem.
Is a Basketball Free-Throw Sequence Nonrandom? A Group Exercise for Undergraduate Statistics Students