Using Dynamic Graphics to Teach the Sampling Distribution with Active Learning

Abstract

The sampling distribution and the Central Limit Theorem (CLT) are the basis for many statistical procedures and inferences. Despite their ubiquitous nature in statistics, these concepts are some of the most abstract and difficult for students to understand. To foster a deeper understanding of these concepts, a web-based application was created that uses dynamic graphics to illustrate the concepts and engage students with active learning. We provide an outline of three in-class activities using the web application to promote the learning of population distributions, simple random sampling, sampling variability, the idea of a statistic, the sampling distribution, the Law of Large Numbers, and the CLT. These in-class activities tie the concepts together and place emphasis on their role as building blocks of statistical inference. By linking abstract theoretical concepts together before introducing statistical inference, the web application facilitates statistical thinking that students can utilize both inside and outside the classroom.

Keywords

• dynamic graphics
• sampling distribution
• active learning
• web application
• central limit theorem