This paper is from Session 4A: Randomisation and bootstrapping: the quick way to inference

which comes under Topic 4: Statistics education at the post-secondary level

Intuitive introduction to the important ideas of inference

Presenter

• Robin Lock (St. Lawrence University, United States)

Co-authors

• Dennis Lock (Iowa State University, United States)
• Eric Lock (Duke University, United States)
• Kari Lock Morgan (Duke University, United States)
• Patti Frazer Lock (St. Lawrence University, United States)

Abstract

Concepts of statistical inference, such as margin of error when estimating a parameter and p-value when testing a hypothesis, are notoriously difficult for students to grasp. In traditional approaches, these ideas typically come as the culmination of a long development of prerequisite material on sampling distributions, formulas for standard errors, standard reference distributions, central limit theorems, and formulas for standardizing values. Simulation methods, such as bootstrap intervals and randomization tests, require minimal background knowledge and highlight the underlying logic of statistical inference, giving students an intuitive appreciation for the key ideas early in a course. But are such methods accessible and understandable to beginning students? We argue that advances in technology make this approach both feasible and desirable. So how does one go about modifying a course to incorporate these ideas? That question is the main focus of this paper.