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I am now convinced that Google searches are the most important dataset ever collected on the human psyche.
At Google, major decisions are based on only a tiny sampling of all their data. You don’t always need a ton of data to find important insights. You need the right data. A major reason that Google searches are so valuable is not that there are so many of them; it is that people are so honest in them. People lie to friends, lovers, doctors, surveys, and themselves. But on Google they might share embarrassing information, about, among other things, their sexless marriages, their mental health issues, their insecurities, and their animosity toward black people.
Kids from better-off backgrounds are more likely to be given common names, such as Kevin, Chris, and John. Kids from difficult homes in the projects are more likely to be given unique names, such as Knowshon, Uneek, and Breionshay. African-American kids born into poverty are nearly twice as likely to have a name that is given to no other child born in that same year.
Among all African-Americans born in the 1980s, about 60 percent had unmarried parents. But I estimate that among African-Americans born in that decade who reached the NBA, a significant majority had married parents. In other words, the NBA is not composed primarily of men with backgrounds like that of LeBron James. There are more men like Chris Bosh, raised by two parents in Texas who cultivated his interest in electronic gadgets, or Chris Paul, the second son of middle-class parents in Lewisville, North Carolina, whose family joined him on an episode of Family Feud in 2011.
The data tells us Jordan is absolutely right to thank his middle-class, married parents. The data tells us that in worse-off families, in worse-off communities, there are NBA-level talents who are not in the NBA. These men had the genes, had the ambition, but never developed the temperament to become basketball superstars.
milliseconds. Goldman and other financial firms paid tens of millions of dollars to get access to fiber-optic cables that reduced the time information travels from Chicago to New Jersey by just four milliseconds (from 17 to 13). Financial firms have algorithms in place to read the information and trade based on it—all in a matter of milliseconds. After this crucial information is released, the market will move in less time than it takes you to blink your eye. So what is this crucial data that is so valuable to Goldman and numerous other financial institutions? The monthly unemployment rate.
First, and perhaps most important, if you are going to try to use new data to revolutionize a field, it is best to go into a field where old methods are lousy.
Women, on average, prefer men who are taller and share their hobbies; men, on average, prefer women who are skinnier and share their hobbies. Nothing new there.
The scientists found that a woman signals her interest by varying her pitch, speaking more softly, and taking shorter turns talking. There are also major clues about a woman’s interest based on the particular words she uses. A woman is unlikely to be interested when she uses hedge words and phrases such as “probably” or “I guess.” Fellas, if a woman is hedging her statements on any topic—if she “sorta” likes her drink or “kinda” feels chilly or “probably” will have another hors d’oeuvre—you can bet that she is “sorta” “kinda” “probably” not into you. A woman is likely to be interested when she
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a woman is more likely to report a connection if a man laughs at her jokes and keeps the conversation on topics she introduces rather than constantly changing the subject to those he wants to talk about.* Women also like men who express support and sympathy. If a man says, “That’s awesome!” or “That’s really cool,” a woman is significantly more likely to report a connection. Likewise if he uses phrases such as “That’s tough” or “You must be sad.”
