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Sequential analysis is a field of statistics focused on methods that evaluate data as it accumulates, enabling decisions—such as stopping, continuing, or modifying a study—before a fixed sample size is reached. Books in this category cover the theory and practice of sequential hypothesis testing and estimation, including stopping rules, error control, and efficiency, with classic approaches like Wald’s sequential probability ratio test alongside modern group sequential and adaptive designs. Topics often include optimal stopping, boundary-crossing probabilities, monitoring and change-point detection, and online decision-making. Applications span clinical trials and biostatistics, industrial quality control and reliability, finance and econometrics, A/B testing and online experimentation, signal processing, and machine learning. The category includes introductory texts, advanced monographs, and practical handbooks that combine rigorous probability theory with design guidance and software-oriented implementation.

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