Statistics for the Life Sciences

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.

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 Ph.D. 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. Myra passed away in 1992.

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

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

UNIT III: INFERENCE FOR CATEGORICAL DATA

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

UNIT IV: MODELING RELATIONSHIPS

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

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