Like many fields of learning, biostatistics has its own vocabulary often seen in medical and public health literature. Phrases like “statistical significance,” “p-value less than 0.05,” “95% confident,” and “margin of error” can have enormous impact in a world that relies on statistics to make decisions: Should Drug A be recommended over Drug B? Should a national policy on X be implemented? Does Vitamin C truly prevent colds? However, do we really know what these terms and phrases mean? Understanding the theory and methodology behind study design, estimation and hypothesis testing is crucial to ensuring that findings and practices in public health and biomedicine are supported by reliable evidence. This course covers the basic tools for the collection, analysis, and presentation of data. Central to these skills is assessing the impact of chance and variability on the interpretation of research findings and subsequent recommendations for public health practice and policy. Topics covered include: general principles of study design; estimation; hypothesis testing; several methods for comparison of discrete and continuous data including the chi-squared test of independence, t-tests, ANOVA, correlation, linear regression and logistic regression. This introductory course is a Core Course for the Biostatistics Department and is mandatory for all Masters level Biostatistics students.
Students who successfully complete this course will be able to: