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Abstract:
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Statistics education for nonstatisticians typically includes introductory univariate and bivariate methods and multivariate regression, with elective work in specific types of advanced models. Along the way, in most programs students learn a variety of not-quite-statistical but still-quite-critical information such as data collection methods, an understanding of bias, and how to identify and account for confounding. However, because many different types of learners are taught in these courses there is almost always some content that is not important for students in some disciplines. In response, non-statistical disciplines sometimes develop their own statistical coursework. When this happens, vital elements may be inadvertently excluded. In addition to reviewing topics for statistics courses, this talk will explore models by which non-statistical disciplines can "personalize" statistics education for their students while leveraging courses taught by statisticians.
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