Combating anti-statistical thinking using simulation-based methods throughout the undergraduate curriculum
Nathan Tintle, Beth Chance, George Cobb, Soma Roy, Todd Swanson and, Jill VanderStoep

TL;DR
This paper advocates for integrating simulation-based inference throughout undergraduate statistics education to enhance understanding, connect concepts, and foster statistical intuition, requiring curriculum reform and innovative pedagogical approaches.
Contribution
It proposes a comprehensive framework for using simulation-based methods across all undergraduate statistics courses to improve statistical thinking and curriculum effectiveness.
Findings
Simulation-based methods improve students' understanding of inference.
Using simulation enhances connections between statistics and probability.
Simulation-based inference fosters deeper engagement with research processes.
Abstract
The use of simulation-based methods for introducing inference is growing in popularity for the Stat 101 course, due in part to increasing evidence of the methods ability to improve students' statistical thinking. This impact comes from simulation-based methods (a) clearly presenting the overarching logic of inference, (b) strengthening ties between statistics and probability or mathematical concepts, (c) encouraging a focus on the entire research process, (d) facilitating student thinking about advanced statistical concepts, (e) allowing more time to explore, do, and talk about real research and messy data, and (f) acting as a firmer foundation on which to build statistical intuition. Thus, we argue that simulation-based inference should be an entry point to an undergraduate statistics program for all students, and that simulation-based inference should be used throughout all…
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Taxonomy
TopicsStatistics Education and Methodologies · Data Analysis with R
