Description
Book SynopsisThis book discusses the theory and practice of teaching biostatistics to students in the life sciences, in particular medical and dental trainees and researchers, as well as its crucial importance to biomedical research and evidence-based health care. Specific tools and resources to biostatistics teaching (e.g., “R shiny”) are described, and how they can be used effectively to increase interaction with students and improve engagement with the subject. The book is structured into three parts: teaching and learning of statistics in medicine and allied health sciences; the move to online learning and online learning methods, especially in light of the impact of COVID-19; and computer tools and resources. It provides a unique insight into teaching biostatistics to medical and dental students from some of the most prominent biostatisticians who also have a very strong interest in biostatistics pedagogy.
Biostatistics teaching is important for maintaining the quality of biomedical research and also in evidence-based medicine, both of which are key to the health and well-being of the world population. This book is particularly useful to readers who are new to the field of biostatistics teaching as well as to more experienced teachers as it presents the latest accounts of the teaching and learning of biostatistics, recent experiences of increased use of online teaching, and useful computer resources and tools for teaching biostatistics.
Table of Contents1. A Survey of Biostatistics Teaching in Medicine and Dentistry in Higher Education in the UK2. Evidence-based practice teaching for undergraduate dental students3. Teaching Medical Statistics within the context of Evidence Based Medicine4. Teaching Null Hypothesis Significance Testing (NHST) in the Health Sciences: The Significance of Significance5. Teaching conceptual understanding of p-values and of confidence intervals, whilst steering away from common misinterpretations 6. Using directed acyclic graphs (DAGs) to represent the data generating mechanisms of disease and healthcare pathways: a guide for educators, students, practitioners and researchers7. Statistics without maths: using Random Sampling to teach Hypothesis Testing.8. COVID-19: Online not distant – MSc students’ feedback on an alternative approach to teaching ‘Research Methods and Introduction to Statistics’ at UCL Queen Square Institute of Neurology.9. Common misconceptions of online statistics teaching 10. Authentic project-based assessment using the Islands: Instructor’s view. 11. An interactive application demonstrating frequentist and Bayesian inferential frame-works12. Teaching data analysis to life scientists using “R” statistical software: challenges, opportunities, and effective methods13. Statistics in a world without science14. Killing me softly with your stats teaching: How much stats is too much stats?15. Life as a medical statistician.
