Introduction to Design and Analysis of Scientific Studies exposes undergraduate and graduate students to 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.
Features:
- Classical experimental design with an emphasis on computation using tidyverse packages in R.
- Applications of experimental design to clinical trials, A/B testing, and other modern examples.
- Discussion of the link between classical experimental design and causal inference.
- The role of randomization in experimental design and sampling in the big data era.
- Exercises with solutions.
Instructor slides in RMarkdown, a
