The advent of electronic communication between students and teachers facilitates a number of new techniques in the teaching of statistics. This article presents the author's experiences with providing each student in a large, multi-section class with a unique dataset for homework and in-class exercises throughout the semester. Each student's sample is pseudo-randomly generated from the same underlying distribution (in the case of hypothesis tests and confidence intervals involving ), or the same underlying linear relationship (in the case of simple linear regression). This approach initially leads students to identify with their individual summary statistics, test results, and fitted models, as "the answer" they would have come up with in an applied setting, while subsequently forcing them to recognize their answers as representing a single observation from some larger sampling distribution.
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