In his book Thinking Statistically, New York-based author Uri Bram details three crucial concepts from statistics for the educated layperson: selection bias, errors that result from drawing conclusions based on non-random samples; endogeneity, errors that result from false assumptions about causality; and Bayesian reasoning, a method of iteratively refining beliefs based on additional evidence. Bram draws from everyday experience to illustrate the concepts, analyzing topics from the profusion of bad performers at amateur talent competitions to why everyone seems to know a successful college drop-out. One reviewer summed up the reading experience amusingly: “Perversely, the best compliment I can give this book is to warn prospective readers against relying on its very high reviews, because of the inherent selection bias in the sample of reviewers relative to the entire population of readers.”
“Thinking Statistically” by Uri Bram
Travis Korte is a research analyst at the Center for Data Innovation specializing in data science applications and open data. He has a background in journalism, computer science and statistics. Prior to joining the Center for Data Innovation, he launched the Science vertical of The Huffington Post and served as its Associate Editor, covering a wide range of science and technology topics. He has worked on data science projects with HuffPost and other organizations. Before this, he graduated with highest honors from the University of California, Berkeley, having studied critical theory and completed coursework in computer science and economics. His research interests are in computational social science and using data to engage with complex social systems. You can follow him on Twitter @traviskorte.
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