A brief, effective tour of applied statistics.The first part covers general principles for successful data analysis, while the second part consists of three- or four-page discussions of real (or nearly-real) datasets.(These discussions aren't worked examples so much as sketches of the authors' recommended analyses for the various datasets.)
I really liked the authors' emphases on descriptive statistics and on sensitivity, and I think they show pretty convincingly that every dataset has its quirks, and requires its own mindset and its own unique analysis.
The book feels dated (which it is!) - there's very little discussion of computationally-intensive analytic techniques - but I learned a lot from the authors' careful, first-principles approach.I wouldn't have understood this book when I first started learning about statistics, but I think I could have benefited from it once I'd worked a handful of applied problems, and I think statisticians much more experienced than I am (that's most of them) would enjoy this book and derive some insights from it.
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