Truth or Truthiness : Distinguishing Fact from Fiction by Learning to Think Like a Data Scientist
Cambridge Univ Pr 2016/02
210 p. 52 b/w illus. 24 cm
Using the tools of causal inference this book evaluates the evidence, or lack thereof, supporting claims in many fields, with special emphasis in education.
Escaping the clutches of truthiness begins with one simple question: 'what is the evidence?' Howard Wainer shows how the sceptical mindset of a data scientist can expose truthiness, nonsense, and outright deception. He evaluates the evidence, or lack thereof, supporting claims in many fields, with special emphasis in education.
Table of Contents
Part I. Thinking Like a Data Scientist: 1. How the rule of 72 can provide guidance to advance your wealth, your career and your gas mileage; 2. Piano virtuosos and the four-minute mile; 3. Happiness and causal inference; 4. Causal inference and death; 5. Using experiments to answer four vexing questions; 6. Causal inferences from observational studies: fracking, injection wells, earthquakes, and Oklahoma; 7. Life follows art: gaming the missing data algorithm; Part II. Communicating Like a Data Scientist: 8. On the crucial role of empathy in the design of communications: genetic testing as an example; 9. Improving data displays: the media's, and ours; 10. Inside-out plots; 11. A century and a half of moral statistics: plotting evidence to affect social policy; Part III. Applying the Tools of Data Science to Education: 12. Waiting for Achilles; 13. How much is tenure worth?; 14. Detecting cheating badly: if it could have been, it must have been; 15. When nothing is not zero: a true saga of missing data, adequate yearly progress, and a Memphis charter school; 16. Musing about changes in the SAT: is the college board getting rid of the bulldog?; 17. For want of a nail: why worthless subscores may be seriously impeding the progress of western civilization.