The book "Model Selection and Multimodel Inference: A Practical Information-Theoretic Approach" by Kenneth P. Burnham and David R. Anderson, in its second edition, introduces graduate students and researchers to the use of information-theoretic approaches for analyzing empirical data. The authors emphasize the importance of selecting a "best" model and ranking and weighting other models, while also advocating multimodel inference for more robust inferences. The second edition includes improvements in presentation, new chapters, and technical material, with over 100 new references. The book covers the fundamentals of information theory, Akaike's Information Criterion (AIC), and various extensions, such as AICc and TIC. It provides practical examples and discusses the principles of parsimony, model selection uncertainty, and multimodel inference. The authors recommend the information-theoretic approach for observational studies and suggest traditional methods for classic experiments. The book is intended for biologists, statisticians, and researchers in various scientific disciplines, offering a unified and rigorous theory for model selection and inference.The book "Model Selection and Multimodel Inference: A Practical Information-Theoretic Approach" by Kenneth P. Burnham and David R. Anderson, in its second edition, introduces graduate students and researchers to the use of information-theoretic approaches for analyzing empirical data. The authors emphasize the importance of selecting a "best" model and ranking and weighting other models, while also advocating multimodel inference for more robust inferences. The second edition includes improvements in presentation, new chapters, and technical material, with over 100 new references. The book covers the fundamentals of information theory, Akaike's Information Criterion (AIC), and various extensions, such as AICc and TIC. It provides practical examples and discusses the principles of parsimony, model selection uncertainty, and multimodel inference. The authors recommend the information-theoretic approach for observational studies and suggest traditional methods for classic experiments. The book is intended for biologists, statisticians, and researchers in various scientific disciplines, offering a unified and rigorous theory for model selection and inference.