© 2020

Statistical Analysis of Empirical Data

Methods for Applied Sciences


  • Guides researchers in physical, biological, and social sciences who are not statistical specialists, but who require the use of statistical methods

  • Covers methods and concepts used/applied in a wide range of scientific applications, and not commonly covered in a first course

  • Provides a source for responding to questions or challenges concerning choices of statistical methods

  • Includes R code and sample datasets


Table of contents

  1. Front Matter
    Pages i-xi
  2. Scott Pardo
    Pages 1-15
  3. Scott Pardo
    Pages 17-20
  4. Scott Pardo
    Pages 21-32
  5. Scott Pardo
    Pages 33-39
  6. Scott Pardo
    Pages 63-73
  7. Scott Pardo
    Pages 93-106
  8. Scott Pardo
    Pages 107-119
  9. Scott Pardo
    Pages 161-167
  10. Scott Pardo
    Pages 169-180
  11. Scott Pardo
    Pages 181-195
  12. Scott Pardo
    Pages 197-207
  13. Scott Pardo
    Pages 209-217
  14. Scott Pardo
    Pages 219-233
  15. Back Matter
    Pages 235-277

About this book


Researchers and students who use empirical investigation in their work must go through the process of selecting statistical methods for analyses, and they are often challenged to justify these selections. This book is designed for readers with limited background in statistical methodology who seek guidance in defending their statistical decision-making in the worlds of research and practice. It is devoted to helping students and scholars find the information they need to select data analytic methods, and to speak knowledgeably about their statistical research processes. Each chapter opens with a conundrum relating to the selection of an analysis, or to explaining the nature of an analysis. Throughout the chapter, the analysis is described, along with some guidance in justifying the choices of that particular method.

Designed to offer statistical knowledge to the non-specialist, this volume can be used in courses on research methods, or for courses on statistical applications to biological, medical, life, social, or physical sciences. It will also be useful to academic and industrial researchers in engineering and in the physical sciences who will benefit from a stronger understanding of how to analyze empirical data. The book is written for those with foundational education in calculus. However, a brief review of fundamental concepts of probability and statistics, together with a primer on some concepts in elementary calculus and matrix algebra, is included. R code and sample datasets are provided.


statistical methods inference confidence assumptions misconceptions modeling predictive analysis ANOVA statistics for engineering statistics for life sciences

Authors and affiliations

  1. 1.Global Medical & Clinical AffairsAscensia Diabetes CareValhallaUSA

About the authors

Scott A. Pardo, Ph.D., is a professional statistician, having worked in a wide variety of industrial contexts, including the U.S. Army Information Systems Command, satellite systems engineering, pharmaceutical development, and medical devices. He is the author of Empirical Modeling and Data Analysis for Engineers and Applied Scientists (Springer 2016). He is a Six Sigma Master Black Belt, an Accredited Professional Statistician (PStat™), and holds a Ph.D. in Industrial and Systems Engineering from the University of Southern California.

Bibliographic information