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
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