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Statistics for the Life Sciences presents the key concepts of statistics as applied to the life sciences, while incorporating tools and themes of modern data analysis. The book emphasizes interpretation of results using real data which facilitates an understanding of statistics and data through the use of graphical data and analysis. It also provides a summary of methods in Chapter 13: "What to do When." The second edition of Statistics for the Life Sciences has been revised to include a number of new sections covering boxplots, probability trees, and probability plots. In addition, chapter 8 has been expanded to include a more complete discussion of issues in data collection, including observational studies vs. experiments, the placebo effect, the use of historical controls vs. active controls, and randomized response sampling. It now includes a new Chapter 10, which includes all of the chi-square test material as well as new sections on Fisher's exact test, McNemar's test, and relative risk and odds ratios. An essential statistics reference for professionals and scientists in biology, agronomy, medical and health sciences, nutrition, pharmacy, animal science, physical education, forestry, and other life sciences.

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• 1 Introduction
• Statistics and the Life Sciences
• Examples and Overview
• 2 Description of Populations and Samples
• Introduction
• Frequency Distributions: Techniques for Data
• Frequency Distributions: Shapes and Examples
• Descriptive Statistics: Measures of Center
• Boxplots
• Measures of Dispersion
• Effect of Transformation of Variables (Optional)
• Samples and Populations: Statistical Inference
• Perspective
• 3 Random Sampling, Probability, and the Binomial Distribution
• Probability and the Life Sciences
• Random Sampling
• Introduction to Probability
• Probability Trees
• Density Curves
• The Binomial Distribution
• Fitting a Binomial Distribution to Data (Optional)
• 4 The Normal Distribution
• Introduction
• The Normal Curves
• Areas Under a Normal Curve
• Assessing Normality
• The Continuity Correction (Optional)
• Perspective
• 5 Sampling Distributions
• Basic Ideas
• Dichotomous Observations
• Quantitative Observations
• Illustration of the Central Limit Theorem (Optional)
• The Normal Approximation to the Binomial Distribution (Optional)
• Perspective
• 6 Confidence Intervals
• Statistical Estimation
• Standard Error of the Mean
• Confidence Interval for u
• Planning a Study to Estimate u
• Conditions for Validity of Estimation Methods
• Confidence Interval for a Population Proportion
• Perspective and Summary
• 7 Comparison of Two Independent Samples
• Introduction
• Standard Error of (y1 - y2)
• Confidence Interval for (u1 - u2)
• Hypothesis Testing: The t-test
• Further Discussion of the t-test
• One-Tailed Tests
• More on Interpretation of Statistical Significance
• Planning for Adequate Power (Optional)
• Student's t: Conditions and Summary
• More on Principles of Testing Hypotheses
• The Wilcoxon-Mann-Whitney Test
• Perspective
• 8 Statistical Principles of Design
• Introduction
• Observational Studies
• Experiments
• Restricted Randomization: Blocking and Stratification
• Levels of Replication
• Sampling Concerns (Optional)
• Perspective
• 9 Comparison of Two Paired Samples
• Introduction
• The Paired-Sample t-Test and Confidence Interval
• The Paired Design
• The Sign Test
• Further Considerations in Paired Experiments
• Perspective
• 10 Analysis of Categorical Data
• Inference for Proportions: The Chi-Square Goodness-of-Fit Test
• The Chi-Square Test for the 2 X 2 Contingency Table
• Independence and Association in a 2 X 2 Contingency Table
• Fisher's Exact Test (Optional)
• The r x k Contingency Table
• Applicability of Methods
• Confidence Interval for a Difference Between Proportions
• Paired Data and 2 X 2 Tables (Optional)
• Relative Risks and the Odds Ratio (Optional)
• Summary of Chi-Square Tests
• 11 Comparing the Means of k Independent Samples
• Introduction
• The Basic Analysis of Variance
• The Analysis of Variance Model (Optional)
• The Global F Test
• Applicability of Methods
• Linear Combinations of Means (Optional)
• Multiple Comparisons (Optional)
• Perspective
• 12 Linear Regression and Correlation
• Introduction
• The Fitted Regression Line
• Parametric Interpretation of Regression: The Linear Model
• Statistical Inference Concerning B1
• Perspective
• Summary of Formulas
• 13 A Summary of Inference Methods
• Introduction
• Data Analysis Samples
• Appendices
• Chapter Notes
• Statistical Tables
• Answers to Selected Exercises
• Index
• Index of Examples