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dc.contributor.authorSchwab-McCoy, Aimeeen_US
dc.date.accessioned2020-02-06T15:50:10Z
dc.date.available2020-02-06T15:50:10Z
dc.date.issued2018-10-26
dc.identifier.otherposter 6
dc.identifier.urihttp://hdl.handle.net/10504/125979
dc.description.abstractIntroduction|Significant 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.en_US
dc.language.isoen_USen_US
dc.publisherCreighton University, Office of Academic Excellence and Assessmenten_US
dc.subject.otherPosteren_US
dc.titlePre-Conceptionsof Statistical Inference in Undergraduate Biostatistics Courses (poster 6)en_US
dc.typePoster
dc.publisher.locationOmaha, Nebraskaen_US
dc.title.workAssessment Symposium, Fall 2018en_US
dc.date.day26
dc.date.year2018
dc.date.monthOctober


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