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.

Differences That Matter—Discriminant Analysis

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  • MANOVA upside down
  • Or left switched with right
  • Which differences separate?

Discriminant analysis asks the opposite questions from MANOVA, the flip side of the coin. It takes the group membership as the dependent variable to look for its predictors (a term that does not imply causality, which can be the source of a misunderstanding with the press). Discriminant analysis can show the strength of the association between group membership and other characteristics, such as a host of different demographic and environmental characteristics. These strengths of association are shown in a series of coefficients, numbers indicating the relative strength of each variable in choosing group membership. As with other complex models that depend on several measurements, be cautious of results, think hard about ...

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