Principles of Statistical Analysis Learning from Randomized Experiments
“Principles of Statistical Analysis Learning from Randomized Experiments” by Ery Arias-Castro is a comprehensive resource published by Cambridge University Press on August 25, 2022. This new edition spans 400 pages and is presented in English, catering to readers with a solid mathematical background who seek to analyze data systematically. The book emphasizes a clear mathematical exposition, facilitating a deep understanding of statistical analysis through an axiomatic and inductive approach.
Readers will find a structured progression from foundational concepts in probability to advanced topics such as statistical inference, simulation, sampling, experimental design, and data collection. The text also addresses practical issues often overlooked in introductory materials, including multiple testing, meta-analysis, and causal inference. This edition serves as a valuable reference for those interested in the intersection of mathematics, statistics, and data analysis within various fields, including artificial intelligence and library sciences.
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This compact course is written for the mathematically literate reader who wants to learn to analyze data in a principled fashion. The language of mathematics enables clear exposition that can go quite deep, quite quickly, and naturally supports an axiomatic and inductive approach to data analysis. Starting with a good grounding in probability, the reader moves to statistical inference via topics of great practical importance – simulation and sampling, as well as experimental design and data collection – that are typically displaced from introductory accounts. The core of the book then covers both standard methods and such advanced topics as multiple testing, meta-analysis, and causal inference.
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