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“Resampling methods” as a book category covers statistical and machine learning techniques that repeatedly draw samples from observed data to quantify uncertainty, test hypotheses, assess model performance, and improve prediction. Books in this genre explain and apply methods such as the bootstrap, permutation (randomization) tests, the jackknife, cross-validation, subsampling, and Monte Carlo resampling. They typically blend theory with computation, detailing algorithms, assumptions, diagnostics, and practical issues like efficiency, bias-variance trade-offs, and handling small samples, high-dimensional data, dependence (time series/spatial), and missingness. Many titles include implementation guidance in R, Python, or other tools, along with case studies from fields such as biostatistics, economics, psychology, and engineering. The category serves students, researchers, and practitioners seeking rigorous yet practical approaches to nonparametric inference, model selection, error estimation, and robust validation.

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