This paper discusses the following features of the author's ideal introductory statistics course: (1) a clear statement of the goals of the course, (2) a careful discussion of the fundamental concept of 'variable', (3) a unification of statistical methods under the concept of a relationship between variables, (4) a characterization of hypothesis testing that is consistent with standard empirical research, (5) the use of practical examples, (6) the right mix of pedagogical techniques: lectures, readings, discussions, exercises, activities, group work, multimedia, (7) a proper choice of computational technology, and (8) a de-emphasis of less important topics such as univariate distributions, probability theory, and the mathematical theory of statistics. The appendices contain (a) recommendations for research to test different approaches to the introductory course and (b) discussion of thought-provoking criticisms of the recommended approach.
The CAUSE Research Group is supported in part by a member initiative grant from the American Statistical Association’s Section on Statistics and Data Science Education