Pre-Conceptionsof Statistical Inference in Undergraduate Biostatistics Courses (poster 6)
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IntroductionSignificant research has been done on improving student learning related to inference in general statistics (Ben-Zvi, Bakker and Makar, 2015; Tintle et al., 2015; Maurer and Lock, 2016; Lane-Getaz, 2017), however there has been little study of students in the health sciences or students taking a first biostatistics course. Students often take biostatistics late in their undergraduate career or during the early stages of a graduate program. These students have typically seen some statistical content before, typically in a research methods course, science course, or lab. Understanding the extent and retention of previous statistical knowledge is necessary to:1. Identify misconceptions that may be different from those seen in a "Stat 101" course.2. Plan interventions to address persisting misconceptions.This project focuses on students' skills interpreting results from NHST procedures, specifically working with p-values. P-values are a controversial topic in statistics, with many academics and editors criticizing their widespread use and abuse (Trafimow and Marks, 2015; Wasserstein and Lazar, 2015). Much of the criticism of p-values is based on their interpretability, so understanding student pre-knowledge and misconceptions of p-values is critical.