Samuels M.L., Witmer J.A., Schaffner A.A. Statistics for the Life Sciences

5th edition. — New York: Pearson, 2018. — 649 p. — ISBN: 978-1-292-10181-1.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.
Statistics for the Life Sciences is an introductory text in statistics, specifically addressed to students specializing in the life sciences. Its primary aims are (1) to show students how statistical reasoning is used in biological, medical, and agricultural research; (2) to enable students to confidently carry out simple statistical analyses and to interpret the results; and (3) to raise students’ awareness of basic statistical issues such as randomization, confounding, and the role of independent replication.Data and Distributions
Introduction
Statistics and the Life Sciences
Types of Evidence
Random Sampling
Description of Samples and PopulationsFrequency Distributions
Descriptive Statistics: Measures of Center
Boxplots
Relationships between Variables
Measures of Dispersion
Effect of Transformation of Variables
Statistical Inference
Perspective
Probability and the Binomial Distribution
Probability and the Life Sciences
Introduction to Probability
Probability Rules
Density Curves
Random Variables
The Binomial Distribution
Fitting a Binomial Distribution to Data
The Normal DistributionThe Normal Curves
Areas under a Normal Curve
Assessing Normality
Perspective
Sampling Distributions
Basic Ideas
The Sample Mean
Illustration of the Central Limit Theorem
The Normal Approximation to the Binomial Distribution
Perspective
Highlights and Study
Inference for Means
Confidence Intervals
Statistical Estimation
Standard Error of the Mean
Confidence Interval for μ
Planning a Study to Estimate μ
Conditions for Validity of Estimation Methods
Comparing Two Means
Confidence Interval for (μ₁ – μ₂)
Perspective and Summary
Comparison of Two Independent Samples
Hypothesis Testing: The Randomization Test
Hypothesis Testing: The t Test
Further Discussion of the t Test
Association and Causation
One-Tailed t Tests
More on Interpretation of Statistical Significance
Planning for Adequate Power
Student’s t: Conditions and Summary
More on Principles of Testing Hypotheses
The Wilcoxon-Mann-Whitney Test
Comparison of Paired SamplesThe Paired-Sample t Test and Confidence Interval
The Paired Design
The Sign Test
The Wilcoxon Signed-Rank Test
Perspective
Highlights and Study
Inference for Categorical Data
Categorical Data: One-Sample Distributions
Dichotomous Observations
Confidence Interval for a Population Proportion
Other Confidence Levels
Inference for Proportions: The Chi-Square Goodness-of-Fit Test
Perspective and Summary
Categorical Data: RelationshipsThe Chi-Square Test for the 2 × 2 Contingency Table
Independence and Association in the 2 × 2 Contingency Table
Fisher’s Exact Test
The r × k Contingency Table
Applicability of Methods
Confidence Interval for Difference Between Probabilities
Paired Data and 2 × 2 Tables
Relative Risk and the Odds Ratio
Summary of Chi-Square Test
Highlights and Study
Modeling Relationships
Comparing the Means of Many Independent SamplesThe Basic One-Way Analysis of Variance
The Analysis of Variance Model
The Global F Test
Applicability of Methods
One-Way Randomized Blocks Design
Two-Way ANOVA
Linear Combinations of Means
Multiple Comparisons
Perspective
Linear Regression and CorrelationThe Correlation Coefficient
The Fitted Regression Line
Parametric Interpretation of Regression: The Linear Model
Statistical Inference Concerning β₁
Guidelines for Interpreting Regression and Correlation
Precision in Prediction
Perspective
Summary of Formulas
Highlights and Study
A Summary of Inference MethodsData Analysis Examples
Chapter Appendices
Chapter Notes
Answers to Selected Exercises
CreditsIndex of Examples