The book "Model Selection and Inference: A Practical Information-Theoretic Approach" by Kenneth P. Burnham and David R. Anderson introduces graduate students and researchers in various scientific disciplines to the use of information-theoretic approaches in analyzing empirical data. The authors emphasize the importance of selecting a "best approximating model" that best represents the inference supported by the data, rather than seeking a single "true model." They focus on Akaike’s Information Criterion (AIC) and its extensions for model selection and statistical inference. The book covers the theoretical foundations of information theory, the computation and interpretation of AIC values, and practical applications through numerous examples. It also discusses the selection of multiple models and the incorporation of model selection uncertainty into parameter estimates and precision. The authors recommend the information-theoretic approach for observational studies, while suggesting traditional methods for experiments. The book is intended for biologists, statisticians, and researchers in the biological, medical, and statistical sciences, providing a unified methodology for model formulation, selection, parameter estimation, and inference.The book "Model Selection and Inference: A Practical Information-Theoretic Approach" by Kenneth P. Burnham and David R. Anderson introduces graduate students and researchers in various scientific disciplines to the use of information-theoretic approaches in analyzing empirical data. The authors emphasize the importance of selecting a "best approximating model" that best represents the inference supported by the data, rather than seeking a single "true model." They focus on Akaike’s Information Criterion (AIC) and its extensions for model selection and statistical inference. The book covers the theoretical foundations of information theory, the computation and interpretation of AIC values, and practical applications through numerous examples. It also discusses the selection of multiple models and the incorporation of model selection uncertainty into parameter estimates and precision. The authors recommend the information-theoretic approach for observational studies, while suggesting traditional methods for experiments. The book is intended for biologists, statisticians, and researchers in the biological, medical, and statistical sciences, providing a unified methodology for model formulation, selection, parameter estimation, and inference.