© 2016

Introduction to Nonparametric Statistics for the Biological Sciences Using R


Table of contents

  1. Front Matter
    Pages i-xv
  2. Thomas W. MacFarland, Jan M. Yates
    Pages 1-50
  3. Thomas W. MacFarland, Jan M. Yates
    Pages 51-76
  4. Thomas W. MacFarland, Jan M. Yates
    Pages 77-102
  5. Thomas W. MacFarland, Jan M. Yates
    Pages 103-132
  6. Thomas W. MacFarland, Jan M. Yates
    Pages 133-175
  7. Thomas W. MacFarland, Jan M. Yates
    Pages 177-211
  8. Thomas W. MacFarland, Jan M. Yates
    Pages 213-247
  9. Thomas W. MacFarland, Jan M. Yates
    Pages 249-297
  10. Thomas W. MacFarland, Jan M. Yates
    Pages 299-326
  11. Back Matter
    Pages 327-329

About this book


This book contains a rich set of tools for nonparametric analyses, and the purpose of this supplemental text is to provide guidance to students and professional researchers on how R is used for nonparametric data analysis in the biological sciences:

  • To introduce when nonparametric approaches to data analysis are appropriate
  • To introduce the leading nonparametric tests commonly used in biostatistics and how R is used to generate appropriate statistics for each test
  • To introduce common figures typically associated with nonparametric data analysis and how R is used to generate appropriate figures in support of each data set

The book focuses on how R is used to distinguish between data that could be classified as nonparametric as opposed to data that could be classified as parametric, with both approaches to data classification covered extensively.

Following an introductory lesson on nonparametric statistics for the biological sciences, the book is organized into eight self-contained lessons on various analyses and tests using R to broadly compare differences between data sets and statistical approach.

This supplemental text is intended for:

  • Upper-level undergraduate and graduate students majoring in the biological sciences, specifically those in agriculture, biology, and health science - both students in lecture-type courses and also those engaged in research projects, such as a master's thesis or a doctoral dissertation
  • And biological researchers at the professional level without a nonparametric statistics background but who regularly work with data more suitable to a nonparametric approach to data analysis


R Nonparametric Normal distribution Mann-Whitney U-Test Wilcoxon Matched-Pairs Signed Ranks Test Kruskal-Wallis H-Test Oneway ANOVA Ranks Life Sciences Biological Sciences Agriculture Biotechnology Plant Science Biostatistics

Authors and affiliations

  1. 1.Office of Institutional EffectivenessNova Southeastern UniversityFort LauderdaleUSA
  2. 2.Abraham S. Fischler College of EducationNova Southeastern UniversityFort LauderdaleUSA

About the authors

Thomas W. MacFarland, Ed.D., is Associate Professor (Computer Technology) at Nova Southeastern University in Fort Lauderdale, Florida.  He joined the Graduate School of Computer and Information Sciences in 1988 and provides consulting services to the university community on research methods and statistical design as well as individual research on institutional concerns and assessment of student learning.  Dr. MacFarland's areas of research include institutional research, assessment of student learning outcomes, federal data resources, and K-12 computer science education.

Jan Yates, Ph.D., is Associate Professor of Educational Media and Computer Science Education at Nova Southeastern University's Abraham S. Fischler College of Education in Fort Lauderdale, Florida. Since 2001, she has worked in the areas of curriculum development, program assessment and review, and accreditation.

Bibliographic information

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