By Catherine Case (University of Georgia)
In their seminal call to reform the use of p-values, Wasserstein, Scherm, and Lazar (2019) acknowledge, “Statistical education will require major changes at all levels to move to a post ‘p<0.05’ world.” This poster outlines how those changes could be implemented in an introductory statistics class using the principles of Backward Design (Wiggins & McTighe, 1998) – a method of curriculum planning with three stages: identifying desired results, determining acceptable evidence, and planning learning experiences and instruction. To identify desired results, the poster translates goals set forth by reformers into outcomes that are achievable in an introductory statistics class. To measure student achievement of those outcomes, the poster provides examples of assessment items that require a nuanced understanding of inference and precise communication skills. Finally, the poster suggests instructional techniques to introduce inference in the spirit of reform and incorporate discussions about the strengths/limitations of various data analysis techniques.