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Basic & Clinical Biostatistics introduces the medical student, researcher, or practitioner to the study of statistics applied to medicine and other disciplines in the health field. The book covers the basics of biostatistics and quantitative methods in epidemiology and the clinical applications in evidence-based medicine and the decision-making methods. Particular emphasis is on study design and interpretation of results of research.


The primary objective of this text is to provide the resources to help the reader become an informed user and consumer of statistics. This book should allow you to:

  • Develop sound judgment about data applicable to clinical care.

  • Read the clinical literature critically, understanding potential errors and fallacies contained therein, and apply confidently the results of medical studies to patient care.

  • Interpret commonly used vital statistics and understand the ramifications of epidemiologic information for patient care and prevention of disease.

  • Reach correct conclusions about diagnostic procedures and laboratory test results.

  • Interpret manufacturers’ information about drugs, instruments, and equipment.

  • Evaluate study protocols and articles submitted for publication and actively participate in clinical research.

  • Develop familiarity with well-known statistical software and interpret the computer output.


The practitioner’s interests, needs, and perspectives in mind during the preparation of this text. Thus, our approach embraces the following features:

  • A genuine medical context is offered for the subject matter. After the introduction to different kinds of studies is presented in Chapter 2, subsequent chapters begin with several Presenting Problems—discussions of studies that have been published in the medical literature. These illustrate the methods discussed in the chapter and in some instances are continued through several chapters and in the exercises to develop a particular line of reasoning more fully.

  • All example articles and datasets are available via open source access.

  • Actual data from the Presenting Problems are used to illustrate the statistical methods.

  • A focus on concepts is accomplished by using computer programs to analyze data and by presenting statistical calculations only to illustrate the logic behind certain statistical methods.

  • The importance of sample size (power analysis) is emphasized, and computer programs to estimate sample size are illustrated.

  • Information is organized from the perspective of the research question being asked.

  • Terms are defined within the relevant text, whenever practical, because biostatistics may be a new language to you. In addition, a glossary of statistical and epidemiologic terms is provided at the end of the book.

  • A table of all symbols used in the book is provided on the inside back cover.

  • A simple classification scheme of study designs used in clinical research is discussed (Chapter 2). We employ this scheme throughout the book as we discuss the Presenting Problems.

  • Flowcharts are used to relate research questions to appropriate statistical methods (inside front cover and Appendix C).

  • A step-by-step explanation of how to read the medical literature critically (Chapter 13)—a necessity for the modern health professional—is provided.

  • Evidence-based medicine and decision-making are addressed in a clinical context (Chapters 3 and 12). Clinicians will be called on increasingly to make decisions based on statistical information.

  • Numerous end-of-chapter Exercises (Chapters 2 through 12) and their complete solutions (Appendix B) are provided.

  • A posttest of multiple-choice questions (Chapter 13) similar to those used in course final examinations or licensure examinations is included.


There are several important enhancements included in the fifth edition.

To facilitate and increase learning, each chapter (except Chapter 1) contains a set of Key Concepts to orient the reader to the important ideas discussed in the chapter.

  • Many of the Presenting Problems have been updated with journal references that require the authors to provide access to the journal article and data through a creative commons license. The links to articles and datasets used for examples are detailed in the Presenting Problem summary at the beginning of each chapter.

  • Material addressing best practices in data visualization is included in Chapter 3.

  • All sample size calculations are now presented using G*Power, an open source program used widely for sample size calculation by researchers.

  • Inclusion of output and exercise answers using R and R Commander—open source statistical applications that may be used across many computer operating systems (Windows, Mac, and Unix).

Susan E. White, PhD

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