Category Archives: 11. Implications of teaching simulation-based methods for undergraduate statistics curricula

Some thoughts and experiences using simulation/randomization based methods in introductory statistics courses and in the undergraduate statistics curriculum

ASchaffnerAndrew Schaffner, Cal Poly, San Luis Obispo

I’m a skeptic. As a mid-career classically trained statistician, for many years  I held tight to the teaching methods used when I was a student: lecture presentations and mathematical arguments to support instruction. For non-calculus based courses I would rely heavily on analogies to bridge concepts (e.g., Behar, et al. Twenty five analogies). Yet even with analogies, students performance on exams and conversations in my office hours often fell short of demonstrating real understanding. I’m waking up. In part because of my work as a co-author with Jeff Witmer, or perhaps because my across-the-hall neighbor is Beth Chance, I’ve finally begun to embrace randomization and simulation methods for classroom instruction.

When working with our majors, … we can take the time to develop foundational understanding with a more in depth randomization curriculum.

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Randomization and the Undergraduate Curriculum

GCobbGeorge Cobb, Mount Holyoke College

I’m writing about implications of simulation-based inference (SBI) for the undergraduate statistics major, but also for students who take only one or a few statistics courses, because these implications apply also to the undergraduate major. I begin with some strengths and omissions of SBI in its current forms.

… the SBI course serves as a foundation for more advanced courses. How does it compare with more traditional first courses? Potentially, it offers better preparation for additional courses, but the details will depend on rethinking the intermediate and advanced curriculum.

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