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

Using simulation/randomization to introduce p-value in week 1

Presenter

• Soma Roy (California Polytechnic State University, United States)

Co-authors

• Allan Rossman (California Polytechnic State University, United States)
• Beth Chance (California Polytechnic State University, United States)
• George Cobb (Mount Holyoke College, United States)
• Jill VanderStoep (Hope College, United States)
• Nathan Tintle (Dordt College, United States)
• Todd Swanson (Hope College, United States)

Abstract

In traditional introductory Statistics courses, statistical inference is often not introduced until the last third of the course, leaving little time for students to develop a strong understanding of the meaning of a p-value. We use simulation and randomization methods to introduce statistical inference early in our introductory Statistics courses, which allows us to discuss concepts of statistical investigations, significance, and p-value in week 1. We start with one-proportion examples to build on students’ intuition about “Is the observed result surprising, if both outcomes are equally likely?” Having established the core concept of the logic of inference, we repeat the cycle for situations involving one mean, two proportions, and so on, to contexts involving several proportions, several means, and two quantitative variables. Here we describe the implementation of this approach by showing examples of our student activities, and demonstrating our use of applets to bolster student understanding and learning.