The Signal and the Noise: Why So Many Predictions Fail-but Some Don't

Nature’s laws do not change very much. So long as the store of human knowledge continues to expand, as it has since Gutenberg’s printing press, we will slowly come to a better understanding of nature’s signals, if never all its secrets. And yet if science and technology are the heroes of this book, there is the risk in the age of Big Data about bec

What isn’t acceptable under Bayes’s theorem is to pretend that you don’t have any prior beliefs. You should work to reduce your biases, but to say you have none is a sign that you have many. To state your beliefs up front—to say “Here’s where I’m coming from”12—is a way to operate in good faith and to recognize that you perceive reality through a s

Bayes’s theorem requires us to state—explicitly—how likely we believe an event is to occur before we begin to weigh the evidence. It calls this estimate a prior belief. Where should our prior beliefs come from? Ideally, we would like to build on our past experience or even better the collective experience of society. This is one of the helpful role

The problem comes when we mistake the approximation for the reality.

This is perhaps the easiest Bayesian principle to apply: make a lot of forecasts. You may not want to stake your company or your livelihood on them, especially at first.* But it’s the only way to get better.

Those who had been forced to make decisions under extreme stress before, like on the battlefield, were more likely to emerge as heroes, leading others to safety.

Of course, markets are not available in every case. It will often be necessary to pick something else as a default. Even common sense can serve as a Bayesian prior, a check against taking the output of a statistical model too credulously. (These models are approximations and often rather crude ones, even if they seem to promise mathematical precisi

We have big brains, but we live in an incomprehensibly large universe. The virtue in thinking probabilistically is that you will force yourself to stop and smell the data—slow down, and consider the imperfections in your thinking. Over time, you should find that this makes your decision making better.

The more often you are willing to test your ideas, the sooner you can begin to avoid these problems and learn from your mistakes. Staring at the ocean and waiting for a flash of insight is how ideas are generated in the movies. In the real world, they rarely come when you are standing in place.13 Nor do the “big” ideas necessarily start out that wa