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"Statistical uncertainty" as a book category covers the theory, methods, and practical implications of uncertainty in data, models, and decisions. It includes introductions to probability and inference, measurement error, sampling variability, confidence intervals and hypothesis testing, Bayesian vs. frequentist perspectives, and advanced topics like uncertainty quantification, sensitivity analysis, model and structural uncertainty, resampling and Monte Carlo methods. Titles may bridge technical textbooks and hands-on guides that teach tools and software for quantifying and visualizing uncertainty, case studies showing how uncertainty shapes scientific conclusions and policy choices, and more accessible works on communicating risk and uncertainty to nonexperts. Intended readers range from students and statisticians to scientists, data scientists, and decision-makers, and the genre emphasizes rigorous reasoning, transparent reporting, and ethical considerations when acting under incomplete information.

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