1st Edition

Design and Analysis of Experiments and Observational Studies using R

By Nathan Taback Copyright 2022
    292 Pages 49 B/W Illustrations
    by Chapman & Hall

    292 Pages 49 B/W Illustrations
    by Chapman & Hall

    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.


    • 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 new R package will be developed to be used with book, and a bookdown version of the book will be freely available. The proposed book will emphasize ethics, communication and decision making as part of design, data analysis, and statistical thinking.

    1 Introduction  2 Mathematical Statistics: Simulation and Computation  3 Comparing Two Treatments  4 Power and Sample Size  5 Comparing More Than Two Treatments  6 Factorial Designs at Two Levels - 2k Designs  7 Causal Inference


    Nathan Taback is Associate Professor of Statistics and Data Science at University of Toronto.