Everybody Lies

by Seth Stephens-Davidowitz

  and truth about the internet, 140, 141–44, 145

  and words as data, 75–76, 93, 95–96

  consumers. See customers/consumers

  contagious behavior, 178

  conversation, and dating, 80–82

  corporations

  consumers blows against, 265

  danger of empowered, 257–65

  reviews of, 265

  correlations

  causation distinguished from, 221–25

  and predicting the stock market, 245–48, 251–52

  counties, zooming in on, 172–90, 239–40

  Country Music Radio, 202

  Craigslist, 117

  creativity, and understanding the world, 280, 281

  crime

  alcohol as contributor to, 196

  and danger of empowered government, 266–70

  and prison conditions, 235

  violent movies and, 193, 194–95, 273

  Cundiff, Billy, 223

  curiosity

  and benefits of digital truth serum, 162, 163

  Levitt views about, 280

  about number of people who finish books, 283–84

  and understanding the world, 280, 281

  cursing, and words as data, 83–85

  customers/consumers

  blows against businesses by, 265

  and price discrimination, 265

  truth about, 153–57

  Cutler, David, 178

  Dahl, Gordon, 191–93, 194–96, 196–97n, 197

  Dale, Stacy, 238

  Dallas, Texas, “Large and Complex Datasets” conference (1977) in, 20–21

  data

  amount/size of, 15, 20–21, 30–31, 53, 171

  benefits of expansion of, 16

  bodies as, 62–74

  collecting the right, 62

  government, 149–50, 266–70

  importance of, 26

  individual-level, 266–70

  as intimidating, 26

  Levitt views about, 280

  as money-maker, 103

  nontraditional sources of, 74

  pictures as, 97–102, 103

  reimagining of what qualifies as, 55–103

  sources of, 14, 15

  speed for transmitting, 55–59

  and understanding the world, 280

  what counts as, 74

  words as, 74–97

  See also Big Data; data science; small data; specific data

  data science

  as changing view of world, 34

  and counterintuitive results, 37–38

  economists role in development of, 228

  future of, 281

  goal of, 37–38

  as intuitive, 26–33

  and who is a data scientist, 27

  dating

  and examples of Big Data searches, 22

  physical appearance and, 82, 120n

  and rejection, 120n

  and Stormfront members, 138–39

  and truth about hate and prejudice, 138–39

  and truth about sex, 120n

  and words as data, 80–86, 103

  Dawn of the Dead (movie), 192

  death, and memorable stories, 33

  DellaVigna, Stefano, 191–93, 194–96, 196–97n, 197

  Democrats

  core principles of, 94

  and origins of political preferences, 170–71

  and words as data, 93–97

  See also specific person or election

  depression

  Google searchs for, 31, 110

  and handling the truth, 158

  and lying, 109, 110

  and parents prejudice against children, 136

  developing countries

  economies of, 101–2, 103

  investing in, 251

  digital truth serum

  abortion and, 147–50

  and child abuse, 145–47, 149–50

  and customers, 153–57

  and Facebook friends, 150–53

  and handling the truth, 158–63

  and hate and prejudice, 128–40

  and ignoring what people tell you, 153–57

  incentives and, 109

  and internet, 140–45

  sex and, 112–28

  sites as, 54

  See also lying; truth

  digital world, randomized experiments in, 210–19

  dimensionality, curse of, 246–52

  discrimination

  and origins of notable Americans, 182–83

  price, 262–65

  See also bias; prejudice; race/racism

  DNA, 248–50

  Dna88 (Stormfront member), 138

  doctors, financial incentives for, 230, 240

  Donato, Adriana, 266, 269

  doppelgangers

  benefits of, 263

  and health, 203–5

  and hunting on social media, 201–3

  and predicting future of baseball players, 197–200, 200n, 203

  and price discrimination, 262–63, 264

  zooming in on, 197–205

  dreams, phallic symbols in, 46–48

  drugs, as addiction, 219

  Duflo, Esther, 208–9, 210, 273

  Earned Income Tax Credit, 178, 179

  economists

  and number of people finishing books, 283

  role in data science development of, 228

  as soft scientists, 273

  See also specific person

  economy/economics

  complexity of, 273

  of developing countries, 101–2, 103

  of Philippines cigarette economy, 102

  and pictures as data, 99–102

  and speed of data, 56–57

  and truth about hate and prejudice, 139

  See also economists; specific topic

  Edmonton, water consumption in, 206

  EDU STAR, 276

  education

  and A/B testing, 276

  and digital revolution, 279

  and overemphasis on measurability, 253–54, 255–56

  in rural India, 209, 210

  small data in, 255–56

  state spending on, 185

  and using online behavior as supplement to testing, 278

  See also high school students; tests/testing

  Eisenhower, Dwight D., 170–71

  elections

  and order of searches, 10–11

  predictions about, 9–14

  voter turn out in, 9–10

  elections, 2008

  and A/B testing, 211–12

  racism in, 2, 6–7, 12, 133, 134

  and Stormfront membership, 139

  elections, 2012

  and A/B testing, 211–12

  predictions about, 10

  racism in, 2–3, 8, 133, 134

  Trump and, 7

  elections, 2016

  and lying, 107

  mapping of, 12–13

  polls about, 1

  predicting outcome of, 10–14

  and racism, 8, 11, 12, 14, 133

  Republican primaries for, 1, 13–14, 133

  and Stormfront membership, 139

  voter turn out in, 11

  electronics company, and advertising, 222, 225, 226

  “Elite Illusion” (Abdulkadiroglu, Angrist, and Pathak), 236

  Ellenberg, Jordan, 283

  Ellerbee, William, 34

  Eng, Jessica, 236–37

  environment, and life expectancy, 177

  EPCOR utility company, 193, 194

  EQB, 63–64

  equality of opportunity, zooming in on, 173–75

  Error Bot, 48–49

  ethics

  and Big Data, 257–65

  and danger of empowered government, 267

  doppelganger searches and, 262–63

  empowered corporations and, 257–65

  and experiments, 226

  hiring practices and, 261–62

  and IQDNA study results, 249


  and paying back loans, 257–61

  and price discrimination, 262–65

  and study of IQ of Facebook users, 261

  Ewing, Patrick, 33

  experiments

  and ethics, 226

  and real science, 272–73

  See also type of experiment or specific experiment

  Facebook

  and A/B testing, 211

  and addictions, 219, 220

  and hiring practices, 261

  and ignoring what people tell you, 153–55, 157

  and influence of childhood experiences data, 166–68, 171

  IQ of users of, 261

  Microsoft-Cambridge University study of users of, 261

  “News Feed” of, 153–55, 255

  and overemphasis on measurability, 254, 255

  and pictures as data, 99

  and “secrets about people,” 155–56

  and size of Big Data, 20

  and small data, 255

  as source of information, 14, 32

  and truth about customers, 153–55

  truth about friends on, 150–53

  and truth about sex, 113–14, 116

  and truth about the internet, 144, 145

  and words as data, 83, 85, 87–88

  The Facebook Effect: The Inside Story of the Company That Is Connecting the World (Kirkpatrick), 154

  Facemash, 156

  faces

  black, 133

  and pictures as data, 98–99

  and truth about hate and prejudice, 133

  Farook, Rizwan, 129–30

  Father’s Day advertising, 222, 225

  50 Shades of Gray, 157

  financial incentives, for doctors, 230, 240

  First Law of Viticulture, 73–74

  food

  and phallic symbols in dreams, 46–48

  predictions about, 71–72

  and pregnancy, 189–90

  football

  and advertising, 221–25

  zooming in on, 196–97n

  Freakonomics (Levitt), 265, 280, 281

  Freud, Sigmund, 22, 45–52, 272, 281

  Friedman, Jerry, 20, 21

  Fryer, Roland, 36

  Gabriel, Stuart, 9–10, 11

  Gallup polls, 2, 88, 113

  gambling/gaming industry, 220–21, 263–65

  “Gangnam Style” video, Psy, 152

  Garland, Judy, 114, 114n

  Gates, Bill, 209, 238–39

  gays

  in closet, 114–15, 116, 117, 118–19, 161

  and dimensions of sexuality, 279

  and examples of Big Data searches, 22

  and handling the truth, 159, 161

  in Iran, 119

  and marriage, 74–76, 93, 115–16, 117

  mobility of, 113–14, 115

  population of, 115, 116, 240

  and pornography, 114–15, 114n, 116, 117, 119

  in Russia, 119

  stereotype of, 114n

  surveys about, 113

  teenagers as, 114, 116

  and truth about hate and prejudice, 129

  and truth about sex, 112–19

  and wives suspicions of husbands, 116–17

  women as, 116

  and words as data, 74–76, 93

  Gelles, Richard, 145

  Gelman, Andrew, 169–70

  gender

  and life expectancy, 176

  and parents prejudice against children, 134–36, 135n

  of Stormfront members, 137

  See also gays

  General Social Survey, 5, 142

  genetics, and IQ, 249–50

  genitals

  and truth about sex, 126–27

  See also penis; vagina

  Gentzkow, Matt, 74–76, 93–97, 141–44

  geography

  zooming in by, 172–90

  See also cities; counties

  Germany, pregnancy in, 190

  Ghana, pregnancy in, 188

  Ghitza, Yair, 169–70

  Ginsberg, Jeremy, 57

  girlfriends, killing, 266, 269

  girls, parents prejudice against young, 134–36

  Gladwell, Malcolm, 29–30

  Gnau, Scott, 264

  gold, price of, 252

  The Goldfinch (Tartt), 283

  Goldman Sachs, 55–56, 59

  Google

  advertisements about, 217–19

  and amount of data, 21

  and digitalizing books, 77

  Mountain View campus of, 59–60, 207

  See also specific topic

  Google AdWords, 3n, 115, 125

  Google Correlate, 57–58

  Google Flu, 57, 57n, 71

  Google Ngrams, 76–77, 78, 79

  Google searches

  advantages of using, 60–62

  auto-complete in, 110–11

  differentiation from other search engines of, 60–62

  as digital truth serum, 109, 110–11

  as dominant source of Big Data, 60

  and the forbidden, 51

  founding of, 60–62

  and hidden thoughts, 110–12

  and honesty/plausibility of data, 9, 53–54

  importance/value of, 14, 21

  polls compared with, 9

  popularity of, 62

  power of, 4–5, 53–54

  and speed of data, 57–58

  and words as data, 76, 88

  See also Big Data; specific search

  Google STD, 71

  Google Trends, 3–4, 3n, 6, 246

  Gottlieb, Joshua, 202, 230

  government

  danger of empowered, 266–70

  and predicting actions of individuals, 266–70

  and privacy issues, 267–70

  spending by, 93, 94

  and trust of data, 149–50

  and words as data, 93, 94

  “Great Body, Great Sex, Great Blowjob” (video), 152, 153

  Great Recession, and child abuse, 145–47

  The Green Monkey (Horse No. 153), 68

  gross domestic product (GDP), and pictures as data, 100–101

  Gross National Happiness, 87, 88

  Guttmacher Institute, 148, 149

  Hannibal (movie), 192, 195

  happiness

  and pictures as data, 99

  See also sentiment analysis

  Harrah’s Casino, 264

  Harris, Tristan, 219–20

  Harry Potter and the Deathly Hallows (Rowling), 88–89, 91

  Hartmann, Wesley R., 225

  Harvard Crimson, editorial about Zuckerberg in, 155

  Harvard University, income of graduates of, 237–39

  hate

  and danger of empowered governments, 266–67, 268–69

  truth about, 128–40, 162–63

  See also prejudice; race/racism

  health

  and alcohol, 207–8

  and comparison of search engines, 71

  and digital revolution, 275–76, 279

  and DNA, 248–49

  and doppelgangers, 203–5

  methodology for studies of, 275–76

  and speed of data transmission, 57

  zooming in on, 203–5, 275

  See also life expectancy

  health insurance, 177

  Henderson, J. Vernon, 99–101

  The Herd with Colin Cowherd, McCaffrey interview on, 196n

  Herzenstein, Michal, 257–61

  Heywood, James, 205

  high school students

  testing of, 231–37, 253–54

  and truth about sex, 114, 116

  high school yearbooks, 98–99

  hiring practices, 261–62

  Hispanics, and Harvard Crimson editorial about Zuckerberg, 155

  Hitler, Adolf, 227

  hockey match, Olympic (2010), 193, 194

  Horse No. 85. See American Pharoah

  Horse No. 153 (The Green Monkey), 68

  horses

&nbs
p; and Bartleby syndrome, 66

  and examples of Big Data searches, 22

  internal organs of, 69–71

  pedigrees of, 66–67, 69, 71

  predicting success of, 62–74, 256

  searches about, 62–74

  hours, zooming in on, 190–97

  housing, price of, 58

  Human Genome Project, 248–49

  Human Rights Campaign, 161

  humankind, data as means for understanding, 16

  humor/jokes, searches for, 18–19

  Hurricane Frances, 71–72

  Hurricane Katrina, 132

  husbands

  wives descriptions of, 160–61, 160–61n

  and wives suspicions about gayness, 116–17

  Hussein, Saddam, 93, 94

  ignoring what people tell you, 153–57

  immigrants, and origins of notable Americans, 184, 186

  implicit association test, 132–34

  incentives, 108, 109

  incest, 50–52, 54, 121

  income distribution, 174–78, 185

  India

  education in rural, 209, 210

  pregnancy in, 187, 188–89

  and sex/porn searches, 19

  Indiana University, and dimensionality study, 247–48

  individuals, predicting the actions of, 266–70

  influenza, data about, 57, 71

  information. See Big Data; data; small data; specific source or search

  Instagram, 99, 151–52, 261

  Internal Revenue Service (IRS), 172, 178–80. See also taxes

  internet

  as addiction, 219–20

  browsing behavior on, 141–44

  as dominated by smut, 151

  segregation on, 141–44

  truth about the, 140–45

  See also A/B testing; social media; specific site

  intuition

  and A/B testing, 214

  and counterintuitive results, 37–38

  data science as, 26–33

  and the dramatic, 33

  as wrong, 31, 32–33

  IQ/intelligence

  and DNA, 249–50

  of Facebook users, 261

  and parents prejudice against children, 135

  Iran, gays in, 119

  Iraq War, 94

  Irresistible (Alter), 219–20

  Islamophobia

  and danger of empowered governments, 266–67, 268–69

  See also Muslims

  Ivy League schools

  income of graduates from, 237–39

  See also specific school

  Jacob, Brian, 254

  James, Bill, 198–99

  James, LeBron, 34, 37, 41, 67

  Jawbone, 277

  Jews, 129, 138

  Ji Hyun Baek, 266

  Jobs, Steve, 185

  Johnson, Earvin III, 67

  Johnson, Lyndon B., 170, 171

  Johnson, “Magic,” 67

  jokes

  and dating, 80–81

  and lying, 109

  nigger, 6, 15, 132, 133, 134

  and truth about hate and prejudice, 132, 133, 134

  Jones, Benjamin F., 227, 228, 276

  Jordan, Jeffrey, 67

  Jordan, Marcus, 67

  Jordan, Michael, 40–41, 67

  Jurafsky, Dan, 80