Biostatistics for Health Sciences

3.0 credits · Graduate

This course provides a comprehensive introduction to statistical inference, equipping students with the skills to critically understand and apply statistical methods to support evidence-based decision-making in Public Health. Through interactive lectures and in-class practice, students will explore key concepts such as probability-based reasoning, hypothesis testing, and statistical fallacies. The course covers essential inferential techniques, including parametric and non-parametric statistical tests, regression models and multivariate analysis. This course provides an introduction to R programming, offering students an opportunity to build foundational computational skills. Assessment includes applied exercises to reinforce understanding of the assumptions and limitations of these methods, along with peer reviews of scientific publications, emphasizing the interpretation of statistical analyses. By the end of the course, students will have developed strong statistical literacy skills essential for evaluating research and making informed decisions in public health practice.