Jessica Chapman (St. Lawrence University), Matt Higham (St. Lawrence University)
Abstract
A course in statistical theory that immediately follows a Probability course is typically required of undergraduate statistics majors. With the recent boom in "data science," how should we update what students learn in such a course to keep pace with how the field of statistics is used today and will be used in the coming years. In this session, we will discuss a few possible ways to help students see how even a theory course can be applied to tackle real statistical problems and issues. Our primary goal of the session is to have a thoughtful discussion with participants about what "might" go into an undergraduate statistical theory course to best enhance our students' statistical thinking skills and to best prepare our undergraduates for careers in our discipline. The intended audience for this breakout session are instructors who have recently taught or who, in the near future, plan on teaching an undergraduate course in mathematical statistics. There is no pre-requisite knowledge or technology requirements, but we would be very excited to hear about others' experience with this course and any useful strategies they have with how to keep the course both relevant and modern.