This Second Edition of The Tao of Statistics: A Path to Understanding (With No Math) provides a reader-friendly approach to statistics in plain English. Unlike other statistics books, this text explains what statistics mean and how they are used, rather than how to calculate them. The book walks readers through basic concepts as well as some of the most complex statistical models in use. The Second Edition adds coverage of big data to better address its impact on p-values and other key concepts; material on small data to show readers how to handle data with fewer data points than optimal; and other new topics like missing data and effect sizes. The book’s two characters (a high school principal and a director of public health) return in the revised edition, with their examples expanded and updated with reference to contemporary concerns in the fields of education and health.

Leeway—Robustness

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  • Data are rowdy
  • Messy, too
  • Tolerance prevails

Surprisingly, statistics generally are quite hardy and easily survive minor violations of their assumptions. This situation (referred to as robustness) is fortunate, because few situations conform entirely to assumptions. For example, real-world data do not conform to precise, normal curves, yet the normal curve supports much of statistics. Assumptions and the magnitude of their tolerable violations are the fodder for much joyous debate among statisticians. This issue becomes particularly rich in opinions when the discussion turns to aggregating assumptions (such as assumptions for the level of measurement combined with those of a statistical technique) for complex models. No one has this problem figured out or has a reasonably overarching approach to it. As a result, many statisticians have ...

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