Some Theory of Sampling

Some Theory of Sampling presents foundational concepts and mathematical frameworks for survey sampling methodology. The text covers probability sampling, estimation procedures, and techniques for measuring sampling errors. The book outlines specific sampling designs including stratified, cluster, and multistage sampling approaches. Mathematical proofs and statistical derivations support the theoretical concepts, while practical examples demonstrate real-world applications. Deming examines sources of errors in sampling, methods for controlling biases, and considerations for questionnaire design and field operations. The work includes detailed formulas and computational procedures for sample size determination and variance estimation. The text stands as a critical contribution to survey methodology and statistical theory, bridging the gap between abstract mathematical concepts and practical survey implementation. Its influence extends beyond statistics into fields like quality control and process improvement.

Readers note this is a dense, mathematical text that requires strong statistical knowledge to follow. Many describe it as one of the most comprehensive treatments of sampling theory available. Likes: - Clear explanations of complex probability concepts - Thorough coverage of sampling methods and applications - Historical value as foundational work in the field - Detailed examples and case studies Dislikes: - Very technical writing style challenging for beginners - Some sections require advanced calculus and statistics background - Outdated examples and applications (published 1950) - Hard to find affordable copies Ratings: Goodreads: 4.4/5 (10 ratings) Amazon: 4.5/5 (6 ratings) "Not for the faint of heart but worth the effort" notes one Amazon reviewer. A Goodreads review states "Deming expects readers to work through complex proofs and derivations - this isn't a practical handbook." The book remains in use for graduate-level sampling courses but is less referenced by practitioners seeking applied methods.

Sampling Techniques by William G. Cochran This text presents comprehensive coverage of probability sampling methods with mathematical foundations and practical applications in survey methodology.

Model Assisted Survey Sampling by Carl-Erik Särndal, Bengt Swensson, and Jan Wretman The book connects classical sampling theory with modern methods using statistical models and estimation techniques.

Survey Sampling by Leslie Kish This work provides systematic coverage of sampling methods, including cluster sampling, stratification, and multistage designs with emphasis on practical implementation.

Introduction to Survey Sampling by Graham Kalton The text establishes fundamental principles of probability sampling with focus on sample design and estimation procedures for various survey contexts.

Sampling: Design and Analysis by Sharon L. Lohr This book integrates sampling theory with modern statistical methods and includes examples from real-world applications across multiple disciplines.

📚 W. Edwards Deming wrote Some Theory of Sampling in 1950 after working with the U.S. Census Bureau, where he revolutionized sampling methods for large populations. 🔍 The book introduced the concept of "sequential sampling," which allows decisions to be made with fewer samples by analyzing data as it's collected rather than waiting for a complete set. 🌏 This work helped Japan rebuild its economy after WWII, as Deming's sampling methods were crucial in maintaining quality control in Japanese manufacturing. 📊 The mathematical principles outlined in the book saved the U.S. government millions of dollars by showing how accurate results could be obtained by sampling just a fraction of the total population. 🎓 Though written over 70 years ago, Some Theory of Sampling remains a foundational text in statistical education and is still used in graduate-level courses worldwide.