"Model Selection and Multimodel Inference: A Practical Information-Theoretic Approach" is a second edition of a book by Kenneth P. Burnham and David R. Anderson, focusing on the use of information-theoretic methods in statistical analysis. The book introduces graduate students and researchers in various scientific disciplines to the concept of model selection and multimodel inference, emphasizing the importance of selecting the best model based on data and ranking and weighting other models in a predefined set. The authors argue that information-theoretic approaches allow for formal inference based on multiple models, leading to more robust conclusions. The second edition includes improvements in the presentation of material, with boxes highlighting key expressions and points, and a new chapter added. Concepts related to multimodel inference are emphasized throughout the book, particularly in Chapters 4, 5, and 6. New technical material has been added to Chapters 5 and 6, with over 100 new references to the technical literature. The book also discusses the Kullback–Leibler distance between models, which is a fundamental quantity in science, and Akaike's information criterion (AIC), a key method for model selection. The authors emphasize that there is no single "true model" in the biological sciences, and modeling is an exercise in approximating the explainable information in empirical data. They advocate for the use of AIC and its extensions for statistical inference, noting that information-theoretic methods provide a more robust approach than traditional statistical null hypothesis testing. The book also discusses the importance of model selection uncertainty and the use of multimodel inference to incorporate this uncertainty into estimates of precision. The authors provide examples and illustrate various technical issues, including comparisons with BIC, a type of dimension consistent criterion. The book is primarily aimed at biologists and statisticians using models for making inferences from empirical data, but it is also useful for researchers in other life sciences, econometrics, the social sciences, and medicine. The book is written in an applied style and is intended for use in courses for students with substantial experience and education in statistics and applied data analysis. The authors acknowledge the contributions of many individuals who helped in the preparation of the book, including colleagues, reviewers, and students. They also thank the Colorado Cooperative Fish and Wildlife Research Unit and other organizations for their support. The book is meant to be relatively easy to read and understand, but the conceptual issues may preclude beginners. The authors encourage readers to have some maturity in the quantitative sciences and experience in data analysis. The book provides a comprehensive overview of information-theoretic methods and their application in statistical inference."Model Selection and Multimodel Inference: A Practical Information-Theoretic Approach" is a second edition of a book by Kenneth P. Burnham and David R. Anderson, focusing on the use of information-theoretic methods in statistical analysis. The book introduces graduate students and researchers in various scientific disciplines to the concept of model selection and multimodel inference, emphasizing the importance of selecting the best model based on data and ranking and weighting other models in a predefined set. The authors argue that information-theoretic approaches allow for formal inference based on multiple models, leading to more robust conclusions. The second edition includes improvements in the presentation of material, with boxes highlighting key expressions and points, and a new chapter added. Concepts related to multimodel inference are emphasized throughout the book, particularly in Chapters 4, 5, and 6. New technical material has been added to Chapters 5 and 6, with over 100 new references to the technical literature. The book also discusses the Kullback–Leibler distance between models, which is a fundamental quantity in science, and Akaike's information criterion (AIC), a key method for model selection. The authors emphasize that there is no single "true model" in the biological sciences, and modeling is an exercise in approximating the explainable information in empirical data. They advocate for the use of AIC and its extensions for statistical inference, noting that information-theoretic methods provide a more robust approach than traditional statistical null hypothesis testing. The book also discusses the importance of model selection uncertainty and the use of multimodel inference to incorporate this uncertainty into estimates of precision. The authors provide examples and illustrate various technical issues, including comparisons with BIC, a type of dimension consistent criterion. The book is primarily aimed at biologists and statisticians using models for making inferences from empirical data, but it is also useful for researchers in other life sciences, econometrics, the social sciences, and medicine. The book is written in an applied style and is intended for use in courses for students with substantial experience and education in statistics and applied data analysis. The authors acknowledge the contributions of many individuals who helped in the preparation of the book, including colleagues, reviewers, and students. They also thank the Colorado Cooperative Fish and Wildlife Research Unit and other organizations for their support. The book is meant to be relatively easy to read and understand, but the conceptual issues may preclude beginners. The authors encourage readers to have some maturity in the quantitative sciences and experience in data analysis. The book provides a comprehensive overview of information-theoretic methods and their application in statistical inference.