Speaker
Description
The reproducibility of research results is a cornerstone of trustworthy science. However, failures to reproduce published findings remain widespread across many disciplines. The way in which data analysis and statistics is taught to students often translates into how they later perform research in labs and clinics. Therefore, improving the reproducibility of biomedical research requires not only better methods, but also a change in the way statistics is taught and practiced. This talk will explore strategies for incorporating reproducible research practices into the statistical education of (bio)statisticians and biomedical researchers. The "Good Statistical Practice" course from the Master Program in Biostatistics at the University of Zurich will serve as a case study in how a course can be designed to equip the next generation of statisticians with competencies in research integrity and reproducible research practices. The talk will also discuss emerging challenges, such as students generating code with artificial intelligence tools.
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