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Statistics for the Life Sciences, 5th edition

Published by Pearson (December 24, 2014) © 2016

  • Myra L. Samuels Purdue University
  • Jeffrey A. Witmer Oberlin College
  • Andrew Schaffner California Polytechnic State University

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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.

  • 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

UNIT I: DATA AND DISTRIBUTIONS

  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

UNIT II: INFERENCE FOR MEANS

  1. 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
  2. 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
  3. 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

UNIT III: INFERENCE FOR CATEGORICAL DATA

  1. 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
  2. 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

UNIT IV: MODELING RELATIONSHIPS

  1. 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
  2. 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
  3. A Summary of Inference Methods
    • 13.1 Introduction
    • 13.2 Data Analysis Examples

Chapter Appendices

Chapter Notes

Statistical Tables

Answers to Selected Exercises

Myra L. Samuels (late) was an Associate Professor of Biostatistics and Epidemiology in Purdue's Department of Veterinary Pathobiology and Associate Director of Statistical Consulting in the Department of Statistics. She received her PhD in Statistics from the University of California–Berkeley, under Jerzy Neyman, and taught at Purdue for 24 years. Her research was oriented toward issues in biostatistics and included both conceptual issues in mathematical statistics and collaborations on applications. Myra was a member of the American Statistical Association, the Biometric Society, and the Society for Clinical Trials. Dr. Samuels passed away in 1992.


Jeff Witmer is Professor of Mathematics at Oberlin College. He received his PhD in Statistics from the University of Minnesota and taught at the University of Florida before coming to Oberlin. He is a Fellow of the American Statistical Association and an elected member of the International Statistics Institute.

Andrew Schaffner is Professor of Statistics at California Polytechnic State University–San Luis Obispo and faculty statistician for the Environmental Biotechnology Institute. He received his PhD in Statistics from the University of Washington. His research involves statistical applications in environmental monitoring.

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