Tuesday, November 12th, 20244:00 pm – 4:30 pm ET
Presented by: Beth Chance (California Polytechnic State University, San Luis Obispo), Karen McGaughey (California Polytechnic State University, San Luis Obispo), Soma Roy (California Polytechnic State University, San Luis Obispo)
Abstract
In this November edition of the JSDSE/CAUSE webinar series, we highlight the 2024 article Simulation-Based Inference: Random Sampling vs. Random Assignment? What Instructors Should Know. “Simulation-based inference” is often considered a pedagogical strategy for helping students develop inferential reasoning, for example, giving them a visual and concrete reference for deciding whether the observed statistic is unlikely to happen by chance alone when the null hypothesis is true. In this webinar, the presenters highlight for teachers some implications of different simulation strategies when analyzing two variables. In particular, does it matter whether the simulation models random sampling or random assignment? They present examples from comparing two means and simple linear regression, highlighting the impact on the standard deviation of the null distribution. They also highlight some possible extensions that simulation-based inference easily allows.
Article Link: https://www.tandfonline.com/doi/full/10.1080/26939169.2024.2333736