Foundational and Applied Statistics for Biologists Using R: 1st Edition (Hardback) book cover

Foundational and Applied Statistics for Biologists Using R

1st Edition

By Ken A. Aho

Chapman and Hall/CRC

618 pages | 130 B/W Illus.

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Full of biological applications, exercises, and interactive graphical examples, Foundational and Applied Statistics for Biologists Using R presents comprehensive coverage of both modern analytical methods and statistical foundations. The author harnesses the inherent properties of the R environment to enable students to examine the code of complicated procedures step by step and thus better understand the process of obtaining analysis results. The graphical capabilities of R are used to provide interactive demonstrations of simple to complex statistical concepts.

Assuming only familiarity with algebra and general calculus, the text offers a flexible structure for both introductory and graduate-level biostatistics courses. The first seven chapters address fundamental topics in statistics, such as the philosophy of science, probability, estimation, hypothesis testing, sampling, and experimental design. The remaining four chapters focus on applications involving correlation, regression, ANOVA, and tabular analyses.

Unlike classic biometric texts, this book provides students with an understanding of the underlying statistics involved in the analysis of biological applications. In particular, it shows how a solid statistical foundation leads to the correct application of procedures, a clear understanding of analyses, and valid inferences concerning biological phenomena.

Web Resource

An R package (asbio) developed by the author is available from CRAN. Accessible to those without prior command-line interface experience, this companion library contains hundreds of functions for statistical pedagogy and biological research. The author’s website also includes an overview of R for novices.


"The book is written in an accessible style for undergraduate students and is built in a lecture-style format. Each chapter commences with a brief description of its contents in the ‘how to read this chapter’ section and finishes with a summary and exercises to help the students practice using the notions that they discovered within the chapter. The book also contains an extensive appendix of mathematical concepts and has the advantage of a large collection of references, which can be accessed for further studies."

Zentralblatt MATH 1306

"The book contains a host of examples to illustrate various methods of analysis and statistical concepts … The statistical concepts described are illustrated with some terrific interactive GUIs and sliders; code to implement these is provided in the R package asbio, which accompanies the text. More than a plaything for the distracted statistician, these are great resources for teaching students and conveying statistical ideas to the non-statistically trained. … does this book offer anything new, not available elsewhere in the crowded market of statistics books using R? Yes it does. The approach, focusing on statistical foundations first, building upwards from basic philosophical concepts, before progressing to implementation and real world applications, is certainly novel. I would, without a doubt, recommend this book to those statisticians working with biologists …"

Statistical Methods in Medical Research, 2015

"This is a terrific intermediate-level modern applied statistics text for biologists or anyone else who is interested in data analysis. … a thorough job of introducing and detailing the main concepts, the methods, and the pleasures of modern data analysis. It is a visually pleasing book with good layouts, nice typefaces, and great tables and graphics and the R code to produce them! A great way for a class to really engage with R graphics. … The author has put effort into making the book. A website and a companion R package, asbio, serve two audiences: introductory classes and more advanced classes. He has succeeded nicely in writing a dual-level book. … I would strongly recommend the book for mature students … I look forward to using it with my upper-level undergrads and the Masters and PhD students I continue to work with."

MAA Reviews, September 2014

Table of Contents


Philosophical and Historical Foundations


Nature of Science

Scientific Principles

Scientific Method

Scientific Hypotheses


Variability and Uncertainty in Investigations

Science and Statistics

Statistics and Biology

Introduction to Probability

Introduction: Models for Random Variables

Classical Probability

Conditional Probability


Combinatorial Analysis

Bayes Rule

Probability Density Functions


Introductory Examples of pdfs

Other Important Distributions

Which pdf to Use?

Reference Tables

Parameters and Statistics




OLS and ML Estimators

Linear Transformations

Bayesian Applications

Interval Estimation: Sampling Distributions, Resampling Distributions, and Simulation Distributions


Sampling Distributions

Confidence Intervals

Resampling Distributions

Bayesian Applications: Simulation Distributions

Hypothesis Testing


Parametric Frequentist Null Hypothesis Testing

Type I and Type II Errors


Criticisms of Frequentist Null Hypothesis Testing

Alternatives to Parametric Null Hypothesis Testing

Alternatives to Null Hypothesis Testing

Sampling Design and Experimental Design


Some Terminology

The Question Is: What Is the Question?

Two Important Tenets: Randomization and Replication

Sampling Design

Experimental Design




Pearson’s Correlation

Robust Correlation

Comparisons of Correlation Procedures



Linear Regression Model

General Linear Models

Simple Linear Regression

Multiple Regression

Fitted and Predicted Values

Confidence and Prediction Intervals

Coefficient of Determination and Important Variants

Power, Sample Size, and Effect Size

Assumptions and Diagnostics for Linear Regression

Transformation in the Context of Linear Models

Fixing the Y-Intercept

Weighted Least Squares

Polynomial Regression

Comparing Model Slopes

Likelihood and General Linear Models

Model Selection

Robust Regression

Model II Regression (X Not Fixed)

Generalized Linear Models

Nonlinear Models

Smoother Approaches to Association and Regression

Bayesian Approaches to Regression




Inferences for Factor Levels

ANOVA as a General Linear Model

Random Effects

Power, Sample Size, and Effect Size

ANOVA Diagnostics and Assumptions

Two-Way Factorial Design

Randomized Block Design

Nested Design

Split-Plot Design

Repeated Measures Design


Unbalanced Designs

Robust ANOVA

Bayesian Approaches to ANOVA

Tabular Analyses


Probability Distributions for Tabular Analyses

One-Way Formats

Confidence Intervals for p

Contingency Tables

Two-Way Tables

Ordinal Variables

Power, Sample Size, and Effect Size

Three-Way Tables

Generalized Linear Models




A Summary and Exercises appear at the end of each chapter.

About the Originator


Subject Categories

BISAC Subject Codes/Headings:
MATHEMATICS / Probability & Statistics / General
MEDICAL / Biostatistics
SCIENCE / Life Sciences / Biology / General