A Classroom Simulation Based on Political Polling To Help Students Understand Sampling Distributions

Abstract

Sampling distributions are central to understanding statistical inference, yet they are one of the most difficult concepts for introductory statistics students. Although hands-on teaching methods are preferred, finding the right balance between theory and practical experience has not been easy. Simulation activities have not always captured the research situations that statisticians work with. This paper describes a method developed by the author to teach sampling distributions using a collaborative learning simulation based on political polling. Anecdotally, students found the polling scenario easy to understand, interesting, and enjoyable, and they were able to explain the meaning of sample results and inferences about the population. Sample examination questions are included, with examples of students' responses that suggest that the method helped them to understand sampling error and its role in statistical inference.

Keywords:

• Collaborative learning
• Introductory statistics
• Math anxiety
• Standard error
• Statistical inference

Acknowledgments

I would like to thank the staff of the Pew Research Center for the People and the Press, Washington, DC, and of the Voter News Service, New York, NY, for their willingness to be interviewed about the methods they use to conduct political and exit polls. This information served as the knowledge base for constructing the polling lessons used in my courses. I would also like to thank the reviewers and associate editor for their comments, which improved the readability and content of the paper.