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Activity Number: 485 - Innovations in Introductory Statistics
Type: Contributed
Date/Time: Wednesday, August 10, 2022 : 2:00 PM to 3:50 PM
Sponsor: Section on Statistics and Data Science Education
Abstract #320908
Title: Teaching Statistical Inference through a Conceptual Lens: A Spin on Existing Methods with Examples
Author(s): Mortaza Jamshidian* and Parsa Jamshidian
Companies: California State University, Fullerton, Mathematics and UCLA Biostatiatics
Keywords: Teaching; Mean inference; Proportion inference; Statistical Software; Z and t distributions; Rguroo Statistical Software

Using software to teach statistical inference in introductory courses opens the door for methods and practices that are more conceptually appealing to students. In line with guidelines presented in the GAISE College Report, this paper demonstrates intuitive approaches to teaching proportion and mean inference that take advantage of statistical software and emphasize conceptual understanding. The paper recommends steering away from the asymptotic-based methods for proportion inference and using the exact binomial method. Regarding mean inference, we propose a more contextualized and simplified process that uses the distribution of the sample mean directly and avoids standardized statistics such as $z$ or $t$. In both the proportion and mean inference contexts, we discuss the benefits of the proposed approaches and provide detailed examples that demonstrate the methods using the Rguroo statistical software.

Authors who are presenting talks have a * after their name.

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