Automated hiring software from some of the largest US employers such as Amazon have preferentially selected men over women.

Perhaps the most important principle in bullshit studies is Brandolini’s principle. Coined by Italian software engineer Alberto Brandolini in 2014, it states: “The amount of energy needed to refute bullshit is an order of magnitude bigger than [that needed] to produce it.” Producing bullshit is a lot less work than cleaning it up. It is also a lot simpler and cheaper to do. A few years before Brandolini formulated his principle, Italian blogger Uriel Fanelli had already noted that, loosely translated, “an idiot can create more bullshit than you could ever hope to refute.”

Lies are designed to lead away from the truth; bullshit is produced with a gross indifference to the truth.

Bullshit involves language, statistical figures, data graphics, and other forms of presentation intended to persuade or impress an audience by distracting, overwhelming, or intimidating them with a blatant disregard for truth, logical coherence, or what information is actually being conveyed.

Harry Frankfurt [...] refined this notion a bit further. He described bullshit as what people create when they try to impress you or persuade you, without any concern for whether what they are saying is true or false, correct or incorrect.

If sufficient rewards are attached to some measure, people will find ways to increase their scores one way or another, and in doing so will undercut the value of the measure for assessing what it was originally designed to assess.

At the turn of the twentieth century, Vietnam was a part of the French colonies collectively known as French Indochina. Hanoi was developing into a prosperous modern city, and its sewer system provided European-style sanitation, mainly for the wealthy white neighborhoods around the city. Unfortunately, the sewer system also provided an ideal breeding ground for rats, which not only emerged from the sewers to terrorize the inhabitants but also spread diseases such as the bubonic plague. In an effort to rid the city of these pests, the colonial bureaucracy hired rat catchers to go into the sewers, and eventually offered a bounty on the creatures, paying a small reward for each rat tail delivered to the colonial offices. Many of the inhabitants of Hanoi were pleased to be a part of this enterprise, and rat tails started pouring in.
Before long, however, the inhabitants of Hanoi started to see tailless rats skulking in the sewers. Apparently, the rat hunters preferred not to kill their prey. Better to slice off the tail for bounty and leave the rats to reproduce, guaranteeing a steady supply of tails in the future. Entrepreneurs imported rats from other cities, and even began raising rats in captivity to harvest their tails. The bounty program failed because people did what people always do at the prospect of a reward: They start gaming the system.

In 2009, Nature published an article that described a new method for predicting flu outbreaks based on the search terms people use when querying Google. Terms such as “fever,” “headache,” “flu symptoms,” and “pharmacies near me” could be used to track the spread of flu across the United States. Not only could these search frequencies and their geolocations be used to predict doctor visits, the method was both faster and cheaper than the epidemiological tracking methods employed by the Centers for Disease Control and Prevention (CDC).

Lies are designed to lead away from the truth; bullshit is produced with a gross indifference to the truth.

A four-year-old is presented with alternative rewards: one marshmallow or two marshmallows. He is told that he can have a single marshmallow anytime—but if he can wait for a while, he can have two marshmallows. The experimenter then leaves the room and measures the amount of time until the child says screw it, and takes the single marshmallow. (After fifteen minutes of open-ended waiting, a child who has not yet given up receives the two-marshmallow reward. But seriously—do you remember how long fifteen minutes seemed at that age?)

If you study one group and assume that your results apply to other groups, this is extrapolation. If you think you are studying one group, but do not manage to obtain a representative sample of that group, this is a different problem. It is a problem so important in statistics that it has a special name: selection bias. Selection bias arises when the individuals that you sample for your study differ systematically from the population of individuals eligible for your study.

The tobacco industry spent decades trying to sow doubts about the evidence linking smoking with cancer.