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  2. “Table A-15: Alternative Measures of Labor Underutilization,” U.S. Bureau ofLabor Statistics, http://www.bls.gov/news.release/empsit.t15.htm.

  3. Jonathan Stray, “Ethics in Data Journalism: Margin of Error in Bureau of La-bor Statistics Reports,” Data Driven Journalism, 15 January 2016, http://datadrivenjournalism.net/news_and_analysis/ethics_in_data_journalism_margin_of_error_in_bureau_of_labor_statistics_rep.

  4. George Cobb, “The Introductory Statistics Course: a Ptolemaic Curriculum,”Technology Innovations in Statistics Education, 1 (2007), http://escholarship.org/uc/item/6hb3k0nz.

  5. James C. Scott, Seeing Like a State (New Haven: Yale University Press, 1998).

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  8. David Niose, “Anti-Intellectualism Is Killing America,” Psychology Today, 23 June2015, https://www.psychologytoday.com/blog/our-humanity-naturally/201506/anti-intellectualism-is-killing-america.

  9. G. Kitson Clark, The Making of Victorian England (New York: Routledge, 1962),

  11. Chris Davis and Matthew Doig, “State Scraps Felon Voter List,” Sarasota Herald-Tribune, 12 July 2004, http://www.heraldtribune.com/article/20040712/NEWS/

  13. Matt Waite, “Handling Data About Race and Ethnicity,” OpenNews Source, 20June 2014, https://source.opennews.org/en-US/learning/handling-data-about-race-and-ethnicity/.

  14. “Sixteenth Decennial Census of the United States, Instructions to Enumerators,Population and Agriculture,” U.S. Census Bureau, 1940, http://www.census.gov/history/pdf/1940instructions.pdf.

  15. Jens Manuel Krogstad and Mark Hugo Lopez, “‘Mexican,’ ‘Hispanic,’ ‘Latin Amer-ican’ Top List of Race Write-ins on the 2010 Census,” Pew Research Center, 4 April2014, http://www.pewresearch.org/fact-tank/2014/04/04/mexican-hispanic-and-latin-american-top-list-of-race-write-ins-on-the-2010-census/.

  16. “Directive No. 15 as Adopted on May 12, 1977,” U.S. Census Bureau, 1977, http://wonder.cdc.gov/wonder/help/populations/bridged-race/directive15.html.

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  18. “Employment Situation Technical Note,” U.S. Bureau of Labor Statistics, 2015,http://www.bls.gov/news.release/empsit.tn.htm.

  19. Neil Irwin and Kevin Quealy, “How Not to Be Misled by the Jobs Report,” TheNew York Times, 1 May 2013, http ://www.nytimes.com/2014/05/02/upshot/how-not-to-be-misled-by-the-jobs-report.html?_r=0.

  20. “How the Government Measures Unemployment,” U.S. Bureau of Labor Statis-tics, 2015, http://www.bls.gov/cps/cps_htgm.htm.

  21. “Employment Situation Technical Note.”

  22. Marianne Durand and Philippe Flajolet, “Loglog Counting of Large Cardinal-ities,” in ESA (2003), 605–617, http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.12.2718.

  23. Sir Arthur Conan Doyle, “The Adventure of the Blanched Soldier,” in The Case-Book of Sherlock Holmes (1927), 54.

  24. Mike Bostock et al., “One Report, Diverging Perspectives,” The New York Times,5 October 2012, http ://www. nytimes.com/interactive/2012/10/05/business/economy/one-report-diverging-perspectives.html.

  25. James Fallows, “Why to Get More Than 1 Newspaper, iPad Edition,” The At-lantic, 22 October 2013, http://www.theatlantic.com/national/archive/2013/10/why-to-get-more-than-1-newspaper-ipad-edition/280772/.

  26. Kypros Kypri et al., “Effects of Restricting Pub Closing Times on Night-timeAssaults in an Australian City,” Addiction, 2 (2011), http://onlinelibrary.wiley.com/enhanced/doi/10.1111/j.1360-0443.2010.03125.x/.

  27. Ibid.

  28. Nate Silver, The Signal and the Noise: Why So Many Predictions Fail—But SomeDon’t (New York: Penguin, 2012), 484.

  29. Ibid.

  30. Sanjoy Mahajan, Street-Fighting Mathematics: The Art of Educated Guessingand Opportunistic Problem Solving (Cambridge: MIT Press, 2010).

  31. Meier and Zabell, “Benjamin Peirce and the Howland Will.”

  32. Ian Hacking, “Telepathy: Origins of Randomization in Experimental Design,” Isis, 3 (1998), http://www.jstor.org/stable/234674.

  33. Gerard E. Dalal, “Why P=0.05?” http://www.jerrydallal.com/LHSP/p05.htm.

  34. Anders Hald, “On the History of Maximum Likelihood in Relation to Inverse Probability and Least Squares,” Statistical Science, 2 (1999), http://www.jstor.org/stable/2676741.

  35. Robert Kass and Adrian Raftery, “Bayes Factors,” Journal of the American Statistical Association, 430 (1995), http://www.jstor.org/stable/2291091.

  36. Sharon Bertsch McGrayne, The Theory That Would Not Die: How Bayes’ Rule Cracked the Enigma Code, Hunted Down Russian Submarines, and Emerged Triumphantfrom Two Centuries of Controversy (New Haven: Yale University Press, 2011).

  37. Steven Raphael and Jens Ludwig, Evaluating Gun Policy: Effects on Crime and Violence (Chicago: Brookings Institution Press, 2003), 251–277, http://home.uchicago.edu/ludwigj/papers/Exile_chapter_2003.pdf.

  38. Ibid.

  39. Steven D. Levitt, “Understanding Why Crime Fell in the 1990s: Four Factors That Explain the Decline and Six That Do Not,” The Journal of Economic Perspectives, 1 (2004).

  40. Raphael and Ludwig, Evaluating Gun Policy: Effects on Crime and Violence.

  41. Kypri et al., “Effects of Restricting Pub Closing Times on Nighttime Assaultsin an Australian City.”

  42. Raphael and Ludwig, Evaluating Gun Policy: Effects on Crime and Violence.

  43. Occupational Mortality: The Registrar General’s Decennial Supplement for England and Wales, 1970–1972 (London: Her Majesty’s Stationery Office, 1978), http://lib.stat.cmu.edu/DASL/Datafiles/SmokingandCancer.html.

  44. Franz H. Messerli, “Chocolate Consumption, Cognitive Function, and Nobel Laureates,” New England Journal of Medicine (2012): 1562–1564.

  45. Greg Mankiw, “A Striking Scatterplot,” 29 March 2011, http://gregmankiw.blogspot.com/2011/03/striking-scatterplot.html.

  46. Ibid.

  47. Christian Rudder, “Exactly What to Say in a First Message,” OKCupid blog,2009, http://blog.okcupid.com/index.php/online-dating-advice-exactly-what-to-say-in-a-first-message/.

  48. Milberger et al, “Tobacco Manufacturers’ Defence Against Plaintiffs’ Claims ofCancer Causation: Throwing Mud at the Wall and Hoping Some of It Will Stick,” Tobacco Control (December 2006): iv17–iv26, http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2563590/.

  49. Andrew Gelman, “Statistics for Cigarette Sellers,” Chance, 3 (2012), http://www.stat.columbia.edu/~gelman/research/published/ChanceEthics4.pdf.

  50. Bikaramjit Mann and Evan Wood, “Confounding in Observational Studies Explained,” The Open Epidemiology Journal (2012), http://benthamopen.com/contents/pdf/TOEPIJ/TOEPIJ-5-18.pdf.

  51. James F. Pagel, Natalie Forister, and Carol Kwiatkowki, “Adolescent Sleep Disturbance and School Performance: The Confounding Variable of Socioeconomics,” Jour-nal of Clinical Sleep Medicine, 1 (2007).

  52. Judea Pearl, Causality: Models, Reasoning, and Inference, 2nd Edition (Cambridge: Cambridge University Press, 2009).

  53. Danial Kaplan, Statistical Modeling: A Fresh Approach, Second Edition (ProjectMosaic, 2012).

  54. John Stuart Mill, A System of Logic, Vol. 1 (: 1843), 455.

  55. Matt Apuzzo and Adam Goldman, “Documents Show NY Police Watched Devout Muslims,” Associated Press, 6 September 2011, http://www.ap.org/Content/AP-In-The-News/2011/Documents-show-NY-police-watched-devout-Muslims.

  56. Philip Kitcher, The Advancement of Science: Science Without Legend, Objectivity Without Illusions (Oxford: Oxford University Press, 1993).

  57. Daniel Kahneman, Thinking Fast and Slow (New York: Farrar, Straus and Giroux, 2013).

  58. Jr. Richards J. Heuer, The Psychology of Intelligence Analysis (: CIA, 1999), https://www.cia.gov/library/center-for-the-study-of-intelligence/csi-publications/books-and-monographs/psychology-of-intelligence-analysis/art11.html.

  59. Charles Sanders Peirce, “Some Consequences of Four Incapacities,” Journal ofSpeculative Philosophy (1868): 140–157.

  60. Tamara Munzner, “Visualization,” in Fundamentals of Computer Graphics, Third Edition, ed. Peter Shirley and Steve Marschner (AK Peters, 2009), 675–707, http://www.cs.ubc.ca/labs/imager/tr/2009/VisChapter/.

  61. Justin McCarthy, “Most Americans Still See Crime Up Over Last Year,” Gallup, 21 November 2014, http://www.gallup.com/poll/179546/americans-crime-last-year.aspx.

  62. Ibid.

  63. Ruth Hamill, Timothy DeCamp Wilson, and Richard E. Nisbett, “Insensitivity to Sample Bias: Generalizing From Atypical Cases,” Journal of Personality and Social Psychology, 4 (1980).

  64. Ibid.

  65. Ibid.

  66. Ibid.

  67. Ibid.

  68. Angela Fagerlin, Catharine Wang, and Peter A. Ubel, “Reducing the Influence of Anecdotal Reasoning on People’s Health Care Decisions: Is a Picture Worth a Thousand Statistics?” Medical Decision Making, 4 (2005).

  69. Stray.

  70. Jessica M. Pollak and Charis E. Kubrin, “Crime in the News: How Crimes, Offenders and Victims Are Portrayed in the Media,” Journal of Criminal Justice andPopular Culture, 1 (2007).

  71. Miguel Ríos, “The Geography of Tweets,” Twitter, 31 May 2013, https://blog.twitter.com/2013/the-geography-of-tweets.

  72. Moritz Stefaner, “The VIZoSPHERE, 2011,” 2011, http://www. visualizing .org/full-screen/29391.

  73. “Special Coverage of the 2014 Midterms,” FiveThirtyEight, 4 November 2014, http://fivethirtyeight.com/live-blog/special-coverage-the-2014-midterms/?#livepress-update-20137747.

  74. The New York Times, “Who Will Win the Senate?” 4 November 2014, http://www.nytimes.com/newsgraphics/2014/senate-model/.

  75. Elke Weber, “From Subjective Probabilities to Decision Weights: The Effect of Asymmetric Loss Functions on the Evaluation of Uncertain Outcomes and Events,”Psychological Bulletin, 2 (1994).

  76. Adam J. L. Harris and Adam Corner, “Communicating Environmental Risks:Clarifying the Severity Effect in Interpretations of Verbal Probability Expressions,” Journal of Experimental Psychology: Learning, Memory, and Cognition, 6 (2011).

  77. Ulrich Hoffrage et al., “Representation Facilitates Reasoning: What Natural Frequencies Are and What They Are Not,” Cognition (2002), http://www.sciencedirect.com/science/article/pii/S0010027702000501.

  78. “Visualizing Smoking Risk,” Stubborn Mule, 21 October 2010, http://www.stubbornmule.net/2010/10/visualizing-smoking-risk/.

  79. Ibid.

  80. Phillip E. Tetlock, Expert Political Judgment: How Good Is It? How Can We Know? (Princeton: Princeton University Press, 2005).

  81. Ibid.

  82. Ibid.

  83. Paul Meehl, Clinical Versus Statistical Prediction: A Theoretical Analysis and a Review of the Evidence (Minneapolis: University of Minnesota, 1954).

  84. Quinn McNemar, “Review of Clinical Versus Statistical Prediction: A Theoretical Analysis and a Review of the Evidence by Paul E. Meehl,” The American Jour-nal of Psychology, 3 (September 1955).

  85. William M. Grove et al., “Clinical Versus Mechanical Prediction: A Meta-analysis,”Psychological Assessment, 1 (2000).

  86. Paul Meehl, “Causes and Effects of My Disturbing Little Book,” Journal of Per sonality Assessment, 3 (1986).

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