This book grew out of a twelve-week course (one term) on the Design of Scientific Studies at the University of Toronto.  The foundations of classical experimental design and observational studies through a modern framework - The Rubin Causal Model.  A causal inference framework is important in design, data collection and analysis since it provides a framework for investigators to readily evaluate study limitations and draw appropriate conclusions.  R is used to implement designs and analyse the data collected.  The book assumes that the reader has taken basic courses in mathematical statistics, and linear models, although the essentials are reviewed briefly in the first chapter.  Some experience using R is helpful although not essential.