The Analysis of Biological Data

3rd Edition (2019) • By Michael C. Whitlock, Dolph Schluter

The Analysis of Biological Data, Third Edition is a comprehensive, student-friendly biostatistics textbook that teaches core statistical reasoning and practical data analysis for biology. Written by Michael C. Whitlock and Dolph Schluter, it is widely used by biology students, researchers, and instructors who need a clear, applied foundation for analyzing real biological datasets.

This edition emphasizes practical interpretation alongside methods such as hypothesis testing, regression, experimental design, and model-based analysis. It is commonly paired with statistical computing workflows (including R) to support visualization, inference, and reproducible analysis in modern biology.

Product Details

• Title: The Analysis of Biological Data
• Edition: 3rd Edition (2019)
• Authors: Michael C. Whitlock; Dolph Schluter
• Publisher: Macmillan Learning
• Publication Date: December 27, 2019
• ISBN-13: 9781319226237
• ISBN-10: 131922623X
• Format: Hardcover
• Pages: 816
• Dimensions: 7.5 × 1.4 × 9.5 inches (19.0 × 3.5 × 24.1 cm)
• Weight: 3.2 lbs (1.45 kg)
• Price: $139.99
• Availability: In Stock | Free Shipping Worldwide

Key Topics (Highlights)

• Describing, visualizing, and interpreting biological datasets
• Probability, sampling, inference, and hypothesis testing
• Regression, correlation, and analysis of variance (ANOVA)
• Generalized linear models and model-based thinking
• Experimental design and data interpretation in biology
• Statistical computing workflows commonly used in biology (including R)

Standout Features

• Practical, biology-first approach to statistics and data analysis
• Real-world datasets and examples that support hands-on learning
• Clear explanations designed for students new to biostatistics
• Strong alignment with undergraduate and graduate biology curricula

Who Should Buy This?

• Undergraduate and graduate students in biology, ecology, and biostatistics
• Researchers analyzing data in genomics, evolution, ecology, and related fields
• Professors teaching statistics or biological data analysis courses
• Professionals applying statistics in biotech, environmental science, and life sciences

About the Authors

Michael C. Whitlock and Dolph Schluter are widely recognized for their work in evolutionary biology, ecology, and statistical thinking for biological research. Their writing emphasizes clarity, practical application, and biological interpretation—making complex statistical concepts accessible to life science learners.

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Build strong biostatistics and data analysis foundations with The Analysis of Biological Data, 3rd Edition.

Overview

A widely adopted biostatistics textbook that teaches statistical reasoning and practical analysis for biology—covering inference, regression, experimental design, and model-based methods commonly used in modern biological research.

Table of Contents (Highlights)

1. Introduction to Statistics in Biology
2. Describing and Visualizing Data
3. Probability and Statistical Inference
4. Hypothesis Testing and P-Values
5. Linear Regression and Correlation
6. Analysis of Variance (ANOVA)
7. Generalized Linear Models
8. Statistical Computing for Biological Data (Including R)
9. Applications in Genomics and Ecology
10. Experimental Design and Data Interpretation

Who Should Use This Book?

• Biology and biostatistics students
• Researchers working with biological datasets
• Instructors teaching biostatistics or data analysis
• Life science professionals applying statistics

Why Buy from BooksGoat?

• Best market pricing
• Free worldwide shipping
• Bulk and drop-shipping support
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FAQs

• What does this book cover? Biostatistics methods and practical biological data analysis, often paired with statistical computing workflows such as R.
• What is The Analysis of Biological Data about? It teaches statistical reasoning and data analysis methods specifically tailored for biological research and datasets.
• Is this useful for research? Yes—it supports rigorous analysis and interpretation of real biological datasets.
• Does it include regression and experimental design? Yes—regression, ANOVA, model-based methods, and experimental design are key topics.
• Is this book suitable for beginners in statistics? Yes, it is designed for biology students learning statistics for the first time.
• Is R programming used in this book?
  Yes, statistical computing workflows commonly used in biology, including R, are integrated.
• Is this useful for research in ecology or genomics?
  Yes, it is commonly used for data analysis in ecology, evolution, and genomics.
• Does it include real datasets and examples?
  Yes, the book uses real biological datasets to illustrate statistical concepts.
• Does this book support modern biological research methods?
  Yes, it aligns well with contemporary data-driven research approaches in biology.