Multivariate Thinking and the Introductory Statistics Course: Preparing Students to Make Sense of a World Full of Observational Data

• Activity Number: 380 - Bringing Intro Stats into a Multivariate and Data-Rich World
• Type: Invited
• Date/Time: Tuesday, July 31, 2018 : 2:00 PM to 3:50 PM
• Sponsor: Section on Statistical Education
• Author(s): Nicholas J. Horton* and Sarah C Anoke and Brendan Seto
• Companies: Amherst College and Harvard TH Chan School of Public Health and Amherst College
• Keywords: confounding; causal inference; data science; multivariate thinking; statistical education; statistical computing

Abstract

We live in a world of ever expanding observational (or what we might call "found") data. To make decisions and disentangle complex relationships in such a data science world, students need some background (defined to broadly include aspects of design, confounding, causal inference, and directed causal graphs). The GAISE College Report enunciated the importance of multivariate thinking as a way to move beyond bivariate thinking (e.g., not ending the intro course with the two sample t-test). But how do such learning outcomes compete with other aspects of statistics knowledge (e.g., inference and p-values) in introductory courses that are already overfull? In this talk I will offer some reflections and guidance about how we might move forward, with specific implications for introductory and intermediate statistics courses.