A FRAMEWORK TO CHARACTERIZE STUDENT DIFFICULTIES IN LEARNING INFERENCE FROM A SIMULATION-BASED APPROACH

Abstract

Although hypothesis testing is ubiquitous in data analysis, research suggests it is commonly misunderstood. Simulation-based inference methods have potential to make student thinking visible, thus providing a valuable lens to analyze developing conceptions about inference. This paper identifies difficulties made visible through simulation-based methods and introduces a framework to characterize the conceptions behind those difficulties. Using the framework, difficulties can be described largely in terms of two challenges. First, students struggle to coordinate the multi-level scheme, which includes the population or true underlying relationship, the distribution of a single sample, and the distribution of statistics collected from multiple samples. Second, students struggle to coordinate two perspectives: the real world where the sample data were collected, and the hypothetical perspective where the null hypothesis is assumed to be true. First published November 2018 at Statistics Education Research Journal Archives