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Frequentist statistics is a category of books focused on the classical approach to statistical inference, where probability is interpreted as long-run relative frequency and methods are evaluated by their performance under repeated sampling. Books in this genre typically cover hypothesis testing, confidence intervals, p-values, estimators and their properties (bias, variance, consistency), maximum likelihood, sampling distributions, and asymptotic theory. They often include practical techniques such as linear and generalized linear models, ANOVA, experimental design, nonparametric methods, and resampling, with applications across science, engineering, and the social sciences. The range spans introductory textbooks and applied guides to rigorous monographs with proofs, emphasizing design and analysis of experiments and surveys, model fitting, and uncertainty assessment via frequentist error rates. These works contrast with Bayesian treatments by avoiding prior distributions and prioritizing long-run error control.

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