Researchers may not be explicit that an experiment studied only men—such information is sometimes buried in a statistical appendix, and sometimes not reported at all. But often a quick investigation will reveal that the study has a blind spot.

The illusion of explanatory depth is a curiosity killer and a trap. If we think we already understand, why go deeper? Why ask questions?

Doubt is a powerful weapon, and statistics are a vulnerable target.

I hope I’ve persuaded you that we shouldn’t be too eager to entrust our decisions to algorithms. But I don’t want to overdo the critique, because we don’t have some infallible alternative way of making decisions. The choice is between algorithms and humans. Some humans are prejudiced. Many humans are frequently tired, harassed, and overworked. And

one of our stubborn defenses against changing our minds is that we’re good at filtering out or dismissing unwelcome information. A curious person, however, enjoys being surprised and hungers for the unexpected. He or she will not be filtering out surprising news, because it’s far too intriguing.

If algorithms are shown a skewed sample of the world, they will reach a skewed conclusion.

Third, superforecasters tended to update their forecasts frequently as new information emerged, which suggests that a receptiveness to new evidence was important. This willingness to adjust predictions is correlated with making better predictions in the first place:

Sampling error is when a randomly chosen sample doesn’t reflect the underlying population purely by chance; sampling bias is when the sample isn’t randomly chosen at all.

Be curious. Look deeper and ask questions.