Statistical reasoning and modeling are of critical importance to modern biology. This textbook introduces fundamental concepts from probability and statistics which will pave the way for the student of biology to become a well-rounded scientist. No previous study of probability or statistics is assumed. Calculus topics are not used extensively in this book, though some integration and differentiation are expected. The calculus prerequisite is primarily intended to assure a certain level of mathematical maturity. This book puts emphasis on examples, which are presented to motivate the theory. The presentation style is concise and self-contained, briefly including the mathematical elements that are needed for studying probability and statistics. The examples are relevant to students in the life sciences with interests in genetics, biology, ecology, health, etc. We believe that aspects of probability theory are of biological interest and that probability underlies the theory of inferential statistics. Thus, we place an equal emphasis on probability and statistics which are both essential for solving and understanding many types of biological problems.

This textbook introduces the basic concepts from probability theory and statistics which are needed for statistical analysis of data encountered in the biological and health sciences. No previous study is required. Advanced mathematical tools, such as integration and differentiation, are kept to a minimum. The emphasis is put on the examples. Probabilistic methods are discussed at length, but the focus of this edition is on statistics.The examples are kept simple, so that the reader can learn quickly and see the usefulness of various statistical and probabilistic methods. Some of the examples used in this book draw attention to various problems related to environmental issues, climate change, loss of bio-diversity, and their impact on wildlife and humans.In comparison with the first edition of the book, this second edition contains additional topics such as power, sample size computation and non-parametric methods, and includes a large collection of new problems, as well as the answers to odd-numbered problems. Several sections of this edition are accompanied by instructions using the programming language R for statistical computing and graphics.The Solution Manual is available upon request for all instructors who adopt this book as a course text. Please send your request to [email protected].