This paper is from Session 8B: Research on developing students’ reasoning using simulation methods for introductory statistical inference: Session 2

which comes under Topic 8: Research in statistics education

From data to decision-making: using simulation and resampling methods to teach inferential concepts

Presenter

• Mia Stephens (JMP Division of SAS, United States)

Co-authors

• Don McCormack (Stonehill College & Brandeis University, United States)
• Robert H Carver (Stonehill College & Brandeis University, United States)

Abstract

It is the year 2014. Despite advances in technology and the availability of interactive software and teaching tools, it is still the norm to use normality-based methods, and even look-up tables, to teach statistical inference. This puts a heavy burden on our students, who must struggle through difficult theory before they’re taught how to make decisions and draw inferences from data. Even then, do they understand what a p-value is or what a confidence interval represents? Is there a better way? Interactive computer simulations and resampling methods can help bridge the gap between graphs and summary statistics and inference, providing a gentler and more natural transition. Until recently, these methods required add-ins, specialized programs or custom code. Today, these techniques are available in mainstream statistical software. In this talk, we illustrate how to use simulations, bootstrapping, and randomization tests in JMP® to introduce sampling distributions and explore core inferential concepts.