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Statistics_ A Very Short Introduction (Very Short Introductions)
A concise tour of modern statistics that reframes the discipline as the exciting technology of extracting meaning and understanding from data rather than tedious arithmetic.
A profile of this book is on the way.
What it’s about
Statistics: A Very Short Introduction dismantles the dusty Victorian image of statistics and replaces it with a vivid portrait of a vibrant, computer-powered discipline that underpins medicine, government, commerce, science, and everyday life. David J. Hand takes a bird's-eye view of the whole field — from simple summaries of data, through the collection of good data, probability, estimation and inference, statistical models and methods, to the transformative role of computing — showing how each idea connects to the others as part of an integrated whole. Rather than teaching mechanical procedures, the book conveys statistical philosophy, the importance of data quality, the meaning of uncertainty, and the power of modern tools to reveal truths invisible to the naked eye. Anyone wanting to understand why statistics matters, how it really works, and why no educated citizen can afford to ignore it will find this an illuminating and surprisingly thrilling introduction.
The through-line
- Who it’s for
- A curious, educated non-specialist who wants to understand what statistics really is and how it shapes the modern world.
- The problem
- Statistics seems opaque, intimidating, and easy to misuse, making it hard to interpret data-driven claims. They feel anxious, mistrustful, or bored by numbers and fear being misled by statistics.
- The plan
- Let go of the old image of statistics as tedious arithmetic.
- Learn to summarize and describe data with simple statistics.
- Understand how to collect and judge the quality of data.
- Grasp the language and laws of probability.
- See how estimation, inference, and testing draw conclusions from data.
- The payoff
- The reader sees statistics as an exciting tool of discovery, reads data-driven claims critically, and understands uncertainty and inference.
See our guide
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Additional reading
- Applied Multiple Regression/Correlation Analysis for the Behavioral Sciences · Cohen, J., & Cohen, P.
The book recommends this text for its excellent, accessible narrative discussions on regression, particularly for explanatory purposes in the social sciences.
- Classical and Modern Regression with Applications · Myers, R.
Cited as an excellent resource for its modern approach to regression analysis, especially its strong treatment of regression diagnostics for checking assumptions and identifying influential data points.
- Multivariate Statistical Methods in Behavioral Research · Bock, R. D.
This text is frequently cited by the author for more advanced or technical explanations of concepts in MANOVA, repeated measures, and step-down analysis.
- The Analysis of Covariance and Alternatives · Huitema, B.
Recommended as a very comprehensive and thorough text for readers wishing to gain a deeper understanding of Analysis of Covariance (ANCOVA).
- Structural Equations with Latent Variables · Bollen, K. A.
The guest-authored chapter on SEM heavily references this book as a key source for understanding fundamental concepts like model identification.
- Hierarchical Linear Models: Applications and Data Analysis Methods · Raudenbush, S., & Bryk, A.
The guest-authored chapter on Hierarchical Linear Modeling (HLM) cites this as the seminal text on the topic and the basis for the HLM software.
- Applied Discriminant Analysis · Huberty, C.
The chapter on Discriminant Analysis introduces this book as an excellent, current, and very thorough resource on the topic.
- Doing Bayesian Data Analysis: A Tutorial with R, JAGS, and Stan · John Kruschke
Recommended for reading 'During this book' to get additional information and a different perspective on Bayesian statistics and modeling.
- Regression and Other Stories · Andrew Gelman, Jennifer Hill, & Aki Vehtari
Recommended for reading 'During this book' as a supplementary text for a broader understanding of regression and Bayesian modeling.
- Statistical Rethinking: A Bayesian Course with Examples in R and Stan · Richard McElreath
Recommended for reading 'After this book' as a next step for readers who have mastered the concepts and wish to deepen their understanding of Bayesian modeling.