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.

Background—Independent Variables

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Independent variables are the groups and background variables that enter into questions about differences. For example, I might want to know whether Democrats and Republicans had different average heights. If so, their group affiliation would be their political party. We want to see whether group affiliation is related to differences in average height, our targeted issue. The variables that are the targets of our questions are the dependent variables (covered in Chapter 26). Independent variables, therefore, form two broad classes: variables of interest (often grouping variables that lead to answers to our questions) and covariates. In most cases, they are handled identically in statistical models and tests. The difference is whether we want to ...

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