Please join us for the Statistics Seminar scheduled from 11 a.m. to noon on Friday, Feb. 23.

Our distinguished guest speaker, Mortaza (Mori) Jamshidian from the California State University, Fullerton, will be present a talk on "Using technology to teach concepts of inference in our introductory statistics courses."

The seminar will be conducted in-person in HOS 380; however, virtual attendance is also available via Zoom. Further details can be found below. 

Abstract: 

With the publication of the Guidelines for Assessment and Instruction in Statistics Education (GAISE 2005, 2016), there has been a significant emphasis on teaching fundamental concepts in introductory statistics courses. Consequently, there has been a surge in recent publications offering various pedagogical approaches to achieve this goal. One approach that has gained prominence recently is the teaching of inference through simulation-based methods. While we acknowledge the value of simulation-based methods in introducing concepts of variability and inference, it's worth noting that most textbooks currently focus on inference based on theory-based methods utilizing the central limit theorem and the normal distribution. This preference may be attributed to the fact that many instructors teaching statistics courses are not entirely comfortable with using simulation-based techniques. Furthermore, instructing students on how to conduct simulations and introducing the associated technical aspects can be time-consuming.

In this presentation, we will explore how we can leverage existing technology, specifically the Rguroo Statistical Software, to provide a fresh perspective on the traditional theory-based methods found in widely used textbooks. Our specific proposal involves incorporating the exact binomial method for proportion inference, eliminating the standardization process in teaching mean inference, and postponing the introduction of the central limit theorem until later in the curriculum on inference.

• Zoom link
• Meeting ID: 964 3113 2983
• Passcode: 31989059