Statistics for the Life Sciences, Global Edition

Myra L. Samuels / Jeffrey A. Witmer / Andrew Schaffner  
Total pages
August 2015
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Statistics for the Life Sciences, Global Edition


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For introductory undergraduate or graduate courses in statistics aimed at life science majors.


Bringing Statistics to Life

The Fifth Edition of Statistics for the Life Sciences uses authentic examples and exercises from a wide variety of life science domains to give statistical concepts personal relevance, enabling students to connect concepts with situations they will encounter outside the classroom. The emphasis on understanding ideas rather than memorizing formulas makes the text ideal for students studying a variety of scientific fields: animal science, agronomy, biology, forestry, health, medicine, nutrition, pharmacy, physical education, zoology and more. In the fifth edition, randomization tests have been moved to the fore to motivate the inference procedures introduced in the text. There are no prerequisites for the text except elementary algebra.



Content and Approach

  • Real data in the examples and exercises provide practical and relevant ways for students to connect concepts to situations they will encounter outside the classroom.
  • Probability theory is included only to support statistics concepts.
  • Students are taught to recognize the importance of an analysis that is appropriate for the research question to be answered, for the statistical design of the study, and for the nature of the underlying distributions.
  • Students are led to recognize the impact on real research of design concepts such as random sampling, randomization, efficiency, and the control of extraneous variation by blocking or adjustment.
  • The text concludes with a summary of all of the inference methods presented in the book and provides exercises that require students to apply all of what they have learned.
  • NEW! Randomization methods have been added at the beginnings of Chapters 7, 8, 10, 11, and 12 to introduce or motivate most inference procedures in the text.


Structured to Foster Success

  • Exercises are designed to focus students’ attention on concepts and interpretations rather than on computation.
  • While statistical software is not required to use this text, there are ample opportunities for students to implement the statistical methods learned using any statistical software package of their choosing. Electronic data files are provided for most examples and exercises. 
  • NEW! Unit Highlights provide a chance for students to connect ideas across multiple chapters. They include reflections, summaries, additional examples, and exercises.


New to this Edition

  • Updated and new exercises appear throughout the book, and many exercises from the previous edition that involved calculation and reading tables have been updated to require interpretation of computer output.
  • Updated examples reflect current research from a variety life science disciplines, replacing many older examples in the previous edition.
  • Unit Highlights were added to provide a necessary opportunity for students to connect ideas across multiple chapters. They include reflections, summaries, additional examples, and exercises.
  • Material on randomization-based inference has been added to introduce or motivate most inference procedures presented in this text. There is now a presentation of randomization methods at the beginnings of Chapters 7, 8, 10, 11, and 12.

Table of Contents


1. Introduction

1.1 Statistics and the Life Sciences

1.2 Types of Evidence

1.3 Random Sampling


2. Description of Samples and Populations

2.1 Introduction

2.2 Frequency Distributions

2.3 Descriptive Statistics: Measures of Center

2.4 Boxplots

2.5 Relationships Between Variables

2.6 Measures of Dispersion

2.7 Effect of Transformation of Variables  

2.8 Statistical Inference

2.9 Perspective


3. Probability and the Binomial Distribution

3.1 Probability and the Life Sciences

3.2 Introduction to Probability

3.3 Probability Rules (Optional)

3.4 Density Curves

3.5 Random Variables

3.6 The Binomial Distribution

3.7 Fitting a Binomial Distribution to Data (Optional)


4. The Normal Distribution

4.1 Introduction

4.2 The Normal Curves

4.3 Areas under a Normal Curve

4.4 Assessing Normality

4.5 Perspective


5. Sampling Distributions

5.1 Basic Ideas

5.2 The Sample Mean

5.3 Illustration of the Central Limit Theorem

5.4 The Normal Approximation to the Binomial Distribution

5.5 Perspective


Unit I Highlights and Study




6. Confidence Intervals

6.1 Statistical Estimation

6.2 Standard Error of the Mean

6.3 Confidence Interval for μ

6.4 Planning a Study to Estimate μ

6.5 Conditions for Validity of Estimation Methods

6.6 Comparing Two Means

6.7 Confidence Interval for (μ1 - μ2)

6.8 Perspective and Summary


7. Comparison of Two Independent Samples

7.1 Hypothesis Testing: The Randomization Test

7.2 Hypothesis Testing: The t Test

7.3 Further Discussion of the t Test

7.4 Association and Causation

7.5 One-Tailed t Tests

7.6 More on Interpretation of Statistical Significance

7.7 Planning for Adequate Power  

7.8 Student’s t: Conditions and Summary

7.9 More on Principles of Testing Hypotheses

7.10 The Wilcoxon-Mann-Whitney Test


8. Comparison of Paired Samples

8.1 Introduction

8.2 The Paired-Sample t Test and Confidence Interval

8.3 The Paired Design

8.4 The Sign Test

8.5 The Wilcoxon Signed-Rank Test

8.6 Perspective


Unit II Highlights and Study



9. Categorical Data: One-Sample Distributions

9.1 Dichotomous Observations

9.2 Confidence Interval for a Population Proportion

9.3 Other Confidence Levels (Optional)

9.4 Inference for Proportions: The Chi-Square Goodness-of-Fit Test

9.5 Perspective and Summary


10. Categorical Data: Relationships

10.1 Introduction

10.2 The Chi-Square Test for the 2 × 2 Contingency Table

10.3 Independence and Association in the 2 × 2 Contingency Table

10.4 Fisher’s Exact Test  

10.5 The r × k Contingency Table

10.6 Applicability of Methods

10.7 Confidence Interval for Difference Between Probabilities

10.8 Paired Data and 2 × 2 Tables  

10.9 Relative Risk and the Odds Ratio  

10.10 Summary of Chi-Square Test


Unit III Highlights and Study



11. Comparing the Means of Many Independent Samples

11.1 Introduction

11.2 The Basic One-Way Analysis of Variance

11.3 The Analysis of Variance Model

11.4 The Global F Test

11.5 Applicability of Methods

11.6 One-Way Randomized Blocks Design

11.7 Two-Way ANOVA

11.8 Linear Combinations of Means  

11.9 Multiple Comparisons  

11.10 Perspective


12. Linear Regression and Correlation

12.1 Introduction

12.2 The Correlation Coefficient

12.3 The Fitted Regression Line

12.4 Parametric Interpretation of Regression: The Linear Model

12.5 Statistical Inference Concerning β1

12.6 Guidelines for Interpreting Regression and Correlation

12.7 Precision in Prediction  

12.8 Perspective

12.9 Summary of Formulas


Unit IV Highlights and Study


13. A Summary of Inference Methods

13.1 Introduction

13.2 Data Analysis Examples


Chapter Appendices

Chapter Notes

Statistical Tables

Answers to Selected Exercises