Biostatistical Analysis: Pearson New International Edition

Jerrold H. Zar  
Total pages
July 2013
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Biostatistical Analysis: Pearson New International Edition


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Zar’s Biostatistical Analysis, Fifth Edition, is the ideal textbook for graduate and undergraduate students seeking practical coverage of statistical analysis methods used by researchers to collect, summarize, analyze and draw conclusions from biological research. The latest edition of this best-selling textbook is both comprehensive and easy to read. It is suitable as an introduction for beginning students and as a comprehensive reference book for biological researchers and for advanced students.


This book is appropriate for a one- or two-semester, junior or graduate-level course in biostatistics, biometry, quantitative biology, or statistics, and assumes a prerequisite of algebra.


  • A broad collection of data-analysis procedures and techniques are presented, covering a wide variety of biological research, such as physiology, genetics, ecology, behavior, morphology.
  • The most comprehensive treatment available includes coverage of the basics of statistical analysis, and also the following topics rarely or never found in statistics books for biologists:
    • Diversity
    • Polynomial regression
    • Multidimensional contingency tables
    • Stepwise regression
    • Nonparametric multiple comparisons
    • Higher order factorial analyses of variance
    • Circular distributions
    • Power and sample size determinations.
  • An orderly organization and presentation of topics, with cross-referencing as appropriate.
  • The readable and accessible approach allows students with no previous statistical background or mathematical expertise beyond simple algebra to understand the material presented.
  • The thoughtful presentation encourages students to think about the value of each statistical technique, as opposed to merely plugging numbers into formulae.
  • The exposition considers complex procedures such as factorial analysis of variance and multiple regression in terms of the interpretation of typical computer output.
  • A wealth of graphs and other figures are integrated to visually support concepts under discussion.
  • A uniquely comprehensive set of statistical tables–more than 40 in all–facilitates statistical analyses without having to consult a separate book. This includes tables that are unique to this book.
  • Worked examples for all major procedures guide readers step-by-step through the techniques, demonstrating each of the important concepts.
  • An extensive bibliography directs readers to further relevant literature.


New to this Edition

This edition includes revised, updated, or enhanced coverage of the following topics.

  • Normal distribution
  • Testing for normality
  • Power in statistical hypothesis testing
  • The underlying assumptions, and their violation, in parametric and nonparametric testing
  • A new introduction of prediction limits and one-tailed confidence limits
  • Analysis of variance
  • Comparing variances
  • Multiple-comparison testing, emphasizing the most highly regarded procedures
  • Randomized-block, repeated-measures, and multivariate analysis of variance
  • Simple and multiple linear regression and correlation
  • Kolmogorov-Smirnov goodness-for-fit testing for ordinal data, both two-tailed and one-tailed, continuous and discrete
  • Contingency-table analysis by chi-square and the Fisher Exact Test
  • The use of binomial distribution


Table of Contents

1. Data: Types and Presentations

2. Populations and Samples

3. Measures of Central Tendency

4. Measures of Variability and Dispersion

5. Probabilities

6. The Normal Distribution

7. One-Sample Hypotheses

8. Two-Sample Hypotheses

9. Paired-Sample Hypotheses

10. Multisample Hypotheses and the Analysis of Variance

11. Multiple Comparisons

12. Two-Factor Analysis of Variance

13. Data Transformations

14. Multiway Factorial Analysis of Variance

15. Nested (Hierarchical) Analysis of Variance

16. Multivariate Analysis of Variance

17. Simple Linear Regression

18. Comparing Simple Linear Regression Equations

19. Simple Linear Correlation

20. Multiple Regression and Correlation

21. Polynomial Regression

22. Testing for Goodness of Fit

23. Contingency Tables

24. Dichotomous Variables

25. Testing for Randomness

26. Circular Distributions: Descriptive Statistics

27. Circular Distributions: Hypothesis Testing

Answers to Exercises

Literature Cited

Author Index

Subject Index