Kadyrov, Akhmad, 227
Kahneman, Daniel, 283
Kane, Thomas, 255
Katz, Lawrence, 243
Kaufmann, Sarah, 236–37
Kawachi, Ichiro, 266
Kayak (website), 265
Kennedy, John F., 170, 171, 227
Kerry, John, 8, 244
King John (Shakespeare), 89–90
King, Martin Luther Jr., 132
King, William Lyon Mackenzie (alias), 138–39
Kinsey, Alfred, 113
Kirkpatrick, David, 154
Klapper, Daniel, 225
Knight, Phil, 157
Kodak, and pictures as data, 99
Kohane, Isaac, 203–5
Krueger, Alan B., 56, 238
Ku Klux Klan, 12, 137
Kubrick, Stanley, 190–91
Kundera, Milan, 233
language
and digital revolution, 274, 279
emphasis in, 94
as key to understanding bias, 74–76
and paying back loans, 259–60
and traditional research methods, 274
and U.S. as united or divided, 78–79
See also words
learning. See education
Lemaire, Alain, 257–61
Levitt, Steven, 36, 222, 254, 280, 281. See also Freakonomics
liberals
and origins of political preferences, 169–71
and parents prejudice against children, 136
and truth about the internet, 140, 141–45
and words as data, 75–76, 93, 95–96
library cards, and lying, 106
life, as imitating art, 190–97
life expectancy, 176–78
Linden, Greg, 203
listening, and dating, 82n
loans, paying back, 257–61
Los Angeles Times, and Obama speech about terrorism, 130
lotteries, 229, 229n
Luca, Michael, 265
Lycos (search engine), 60
lying
and age, 108n
and incentives, 108
and jokes, 109
to ourselves, 107–8, 109
and polls, 107
and pornography, 110
prevalence of, 21, 105–12, 239
and racism, 109
reasons for, 106, 107, 108, 108n
and reimaging data, 103
and search information, 5–6, 12
and sex, 112–28
by Stephens-Davidowitz, 282n
and surveys, 105–7, 108, 108n
and taxes, 180
and voting behavior, 106, 107, 109–10
“white,” 107
See also digital truth serum; truth; specific topic
Ma-Kellams, Christine, 266
Macon County, Alabama, successful/notable Americans from, 183, 186–87
Malik, Tashfeen, 129–30
Manchester University, and dimensionality study, 247–48
Massachusetts Institute of Technology, Pantheon project of, 184–85
Matthews, Dylan, 202–3
McCaffrey, Ed, 196–97n
McFarland, Daniel, 80
McPherson, James, 79
measurability, overemphasis on, 252–56
“Measuring Economic Growth from Outer Space” (Henderson, Storygard, and Weil), 99–101
media
bias of, 22, 74–77, 93–97, 102–3
and examples of Big Data searches, 22
owners of, 96
and truth about hate and prejudice, 130, 131
and truth about the internet, 143
and words as data, 74–77, 93–97
See also specific organization
Medicare, and doctors reimbursements, 230, 240
medicine. See doctors; health
Messing, Solomon, 144
MetaCrawler (search engine), 60
Mexicans, and truth about hate and prejudice, 129
Michel, Jean-Baptiste, 76–77, 78–79
Microsoft
and Cambridge University study about IQ of Facebook users, 261
Columbia University pancreatic cancer study and, 28–29, 30
and typing errors by searchers, 48–50
Milkman, Katherine L., 91–92
Minority Report (movie), 266
Minsky, Marvin, 273
minutes, zooming in on, 190–97
Moneyball, Oakland A’s profile in, 254, 255
Moore, Julianne, 185
Moskovitz, Dustin, 238–39
movies
and advertising, 224–25
and crime, 193, 194–95, 273
violent, 190–97, 273
zooming in on, 190–97
See also specific movie
msnbc.com, 143
murder
and danger of empowered government, 266–67, 268–69
See also violence
Murdoch, Rupert, 96
Murray, Patty, 256
Muslims
and danger of empowered governments, 266–67, 268–69
and truth about hate and prejudice, 129–31, 162–63
Nantz, Jim, 223
National Center for Health Statistics, 181
National Enquirer magazine, 150–51, 152
national identity, 78–79
natural experiments, 226–28, 229–30, 234–37, 239–40
NBA. See basketball
neighbors, and monetary windfalls, 229
Netflix, 156–57, 203, 212
Netzer, Oded, 257–61
New England Patriots-Baltimore Ravens games, 221, 222–24
New Jack City (movie), 191
New York City, Rolling Stones song about, 278
New York magazine, and A/B testing, 212
New York Mets, 165–66, 167, 169, 171
New York Post, and words as data, 96
New York Times
Clinton (Bill) search in, 61
and IQDNA study results, 249
and Obama speech about terrorism, 130
Stephens-Davidowitz’s first column about sex in, 282
Stormfront users and, 137, 140, 145
and truth about internet, 145
types of stories in, 92
vaginal odors story in, 161
and words as data, 95–96
New York Times Company, and words as data, 95–96
New Yorker magazine
Duflo study in, 209
and Stephens-Davidowitz’s doppelganger search, 202
News Corporation, 96
newslibrary.com, 95
Nielsen surveys, 5
Nietzsche, Friedrich, 268
Nigeria, pregnancy in, 188, 189, 190
“nigger”
and hate and prejudice, 6, 7, 131–34, 244
jokes, 6, 15, 132, 133, 134
motivation for searches about, 6
and Obama’s election, 7, 244
and power of Big Data, 15
prevalence of searches about, 6
and Trump’s election, 14
night light, and pictures as data, 100–101
Nike, 157
Nixon, Richard M., 170, 171
numbers, obsessive infatuation with, 252–56
Obama, Barack
and A/B testing, 211–14
campaign home page for, 212–14
elections of 2008 and, 2, 6–7, 133, 134, 211–12
elections of 2012 and, 8–9, 10, 133, 134, 211–12
and racism in America, 2, 6–7, 8–9, 12, 134, 240, 243–44
State of the Union (2014) speech of, 159–60
and truth about hate and prejudice, 130–31, 133, 134, 162–63
Ocala horse auction, 65–66, 67, 69
Oedipal complex, Freud theory of, 50–51
OkCupid (dating site), 139
Olken, Benjamin A., 227, 228
127 Hours (movie), 90, 91
Optimal Decisions Group, 262
Or, Flora, 266
Ortiz, David “Big
Papi,” 197–200, 200n, 203
“out-of-sample” tests, 250–51
Page, Larry, 60, 61, 62, 103
pancreatic cancer, Columbia University-Microsoft study of, 28–29
Pandora, 203
Pantheon project (Massachusetts Institute of Technology), 184–85
parents/parenting
and child abuse, 145–47, 149–50, 161
and examples of Big Data searches, 22
and prejudice against children, 134–36, 135n
Parks, Rosa, 93, 94
Parr, Ben, 153–54
Pathak, Parag, 235–36
PatientsLikeMe.com, 205
patterns, and data science as intuitive, 27, 33
Paul, Chris, 37
paying back loans, 257–61
PECOTA model, 199–200, 200n
pedigrees
of basketball players, 67
of horses, 66–67, 69, 71
pedometer, Chance emphasis on, 252–53
penis
and Freud’s theories, 46
and phallic symbols in dreams, 46–47
size of, 17, 19, 123–24, 124n, 127
“penistrian,” 45, 46, 48, 50
Pennsylvania State University, income of graduates of, 237–39
Peysakhovich, Alex, 254
phallic symbols, in dreams, 46–48
Philadelphia Daily News, and words as data, 95
Philippines, cigarette economy in, 102
physical appearance
and dating, 82, 120n
and parents prejudice against children, 135–36
and truth about sex, 120, 120n, 125–26, 127
physics, as science, 272–73
pictures, as data, 97–102, 103
Pierson, Emma, 160n
Piketty, Thomas, 283
Pinky Pizwaanski (horse), 70
pizza, information about, 77
PlentyOfFish (dating site), 139
Plomin, Robert, 249–50
political science, and digital revolution, 244, 274
politics
and A/B testing, 211–14
complexity of, 273
and ignoring what people tell you, 157
and origin of political preferences, 169–71
and truth about the internet, 140–44
and words as data, 95–97
See also conservatives; Democrats; liberals; Republicans
polls
Google searches compared with, 9
and lying, 107
reliability of, 12
See also specific poll or topic
Pop-Tarts, 72
Popp, Noah, 202
Popper, Karl, 45, 272, 273
PornHub (website), 14, 50–52, 54, 116, 120–22, 274
pornography
as addiction, 219
and bias of social media, 151
and breastfeeding, 19
cartoon, 52
child, 121
and digital revolution, 279
and gays, 114–15, 114n, 116, 117, 119
honesty of data about, 53–54
and incest, 50–52
in India, 19
and lying, 110
popular videos on, 152
popularity of, 53, 151
and power of Big Data, 53
search engines for, 61n
and truth about sex, 114–15, 117
unemployed and, 58, 59
Posada, Jorge, 200
poverty
and life expectancy, 176–78
and words as data, 93, 94
See also income distribution
predictions
and data science as intuitive, 27
and getting the numbers right, 74
and what counts as data, 74
and what vs. why it works, 71
See also specific topic
pregnancy, 20, 187–90
prejudice
implicit, 132–34
of parents against children, 134–36, 135n
subconscious, 134, 163
truth about, 128–40, 162–63
See also bias; hate; race/racism; Stormfront
Premise, 101–2, 103
price discrimination, 262–65
prison conditions, and crime, 235
privacy issues, and danger of empowered government, 267–70
property rights, and words as data, 93, 94
proquest.com, 95
Prosper (lending site), 257
Psy, “Gangnam Style” video of, 152
psychics, 266
psychology
and digital revolution, 274, 277–78, 279
as science, 273
as soft science, 273
and traditional research methods, 274
Quantcast, 137
questions
asking the right, 21–22
and dating, 82–83
race/racism
causes of, 18–19
elections of 2008 and, 2, 6–7, 12, 133
elections of 2012 and, 2–3, 8, 133
elections of 2016 and, 8, 11, 12, 14, 133
explicit, 133, 134
and Harvard Crimson editorial about Zuckerberg, 155
and lying, 109
map of, 7–9
and Obama, 2, 6–7, 8–9, 12, 133, 240, 243–44
and predicting success in basketball, 35, 36–37
and Republicans, 3, 7, 8
Stephens-Davidowitz’s study of, 2–3, 6–7, 12, 14, 243–44
and Trump, 8, 9, 11, 12, 14, 133
and truth about hate and prejudice, 129–34, 162–63
See also Muslims; “nigger”
randomized controlled experiments
and A/B testing, 209–21
and causality, 208–9
rape, 121–22, 190–91
Rawlings, Craig, 80
“rawtube” (porn site), 59
Reagan, Andy, 88, 90, 91
Reagan, Ronald, 227
regression discontinuity, 234–36
Reisinger, Joseph, 101–2, 103
relationships, lasting, 31–33
religion, and life expectancy, 177
Renaissance (hedge fund), 246
Republicans
core principles of, 94
and origins of political preferences, 170–71
and racism, 3, 7, 8
and words as data, 93–97
See also specific person or election
research
and expansion of research methodology, 275–76
See also specific researcher or research
reviews, of businesses, 265
“Rocket Tube” (gay porn site), 115
Rolling Stones, 278
Romney, Mitt, 10, 212
Roseau County, Minnesota, successful/notable Americans from, 186, 187
Runaway Bride (movie), 192, 195
sabermetricians, 198–99
San Bernardino, California, shooting in, 129–30
Sands, Emily, 202
science
and Big Data, 273
and experiments, 272–73
real, 272–73
at scale, 276
soft, 273
search engines
differentiation of Google from other, 60–62
for pornography, 61n
reliability of, 60
word-count, 71
See also specific engine
searchers, typing errors by, 48–50
searches
negative words used in, 128–29
See also specific search
“secrets about people,” 155–56
Seder, Jeff, 63–66, 68–70, 71, 74, 155, 256
segregation, 141–44. See also bias; discrimination; race/racism
self-employed people, and taxes, 178–80
sentiment analysis, 87–92, 247–48
sex
as addiction, 219
and benefits of digital truth serum, 158–59, 161
and ch
ildhood experiences, 50–52
condoms and, 5, 122
and digital revolution, 274, 279
and dimensions of sexuality, 279
during marriage, 5–6
and fetishes, 120
and Freud, 45–52
Google searches about, 5–6, 51–52, 114, 115, 117, 118, 122–24, 126, 127–28
and handling the truth, 158–59, 161
and Harvard Crimson editorial about Zuckerberg, 155
how much, 122–23, 124–25, 127
in India, 19
new information about, 19
oral, 128
and physical appearance, 120, 120n, 125–26, 127
and power of Big Data, 53
pregancy and having, 189
Rolling Stones song about, 278
and sex organs, 123–24
Stephens-Davidowitz’s first New York Times column about, 282
and traditional research methods, 274
truth about, 5–6, 112–28, 114n, 117
and typing errors, 48–50
and women’s genitals, 126–27
See also incest; penis; pornography; rape; vagina
Shadow (app), 47
Shakespeare, William, 89–90
Shapiro, Jesse, 74–76, 93–97, 141–44, 235, 273
“Shattered” (Rolling Stones song), 278
shopping habits, predictions about, 71–74
The Signal and the Noise (Silver), 254
Silver, Nate, 10, 12–13, 133, 199, 200, 254, 255
Simmons, Bill, 197–98
Singapore, pregnancy in, 190
Siroker, Dan, 211–12
sleep
and digital revolution, 279
Jawbone and, 276–77
and pregnancy, 189
“Slutload,” 58
small data, 255–56
smiles, and pictures as data, 99
Smith, Michael D., 224
Snow, John, 275
Sochi, Russia, gays in, 119
social media
bias of data from, 150–53
doppelganger hunting on, 201–3
and wives descriptions of husbands, 160–61, 160–61n
See also specific site or topic
social science, 272–74, 276, 279
social security, and words as data, 93
socioeconomic background
and predicting success in basketball, 34–41
See also pedigrees
sociology, 273, 274
Soltas, Evan, 130, 162, 266–67
South Africa, pregnancy in, 189
Southern Poverty Law Center, 137
Spain, pregnancy in, 190
Spartanburg Herald-Journal (South Carolina), and words as data, 96
specialization, extreme, 186
speed, for transmitting data, 56–59
“Spider Solitaire,” 58
Stephens-Davidowitz, Noah, 165–66, 165–66n, 169, 206, 263
Stephens-Davidowitz, Seth
ambitions of, 33
lying by, 282n
mate choice for, 25–26, 271
motivations of, 2
obsessiveness of, 282, 282n
professional background of, 14
and writing conclusions, 271–72, 279, 280–84
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