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.
- What was the cause?
- How do you know?
Covariates are traits or issues that might interfere with arriving at fairly correct results. They are the statistical incarnation of what would be confounds if they were not accommodated. Covariates help answer the question “How do you know it wasn’t such-and-such that caused that-and-which?” Those questions often are excellent, and good research is required to be able to answer them appropriately. Covariates, then, are those characteristics that need to be adjusted out or controlled for when we use statistics to answer our questions in all but the most contrived environments. They are used in such methods as analysis of covariance, multiple regression, discriminant analysis, and canonical covariance analysis (all covered later).
Mostly, covariates spring from not having ...