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

by Nate Silver

We face danger whenever information growth outpaces our understanding of how to process it.

if the quantity of information is increasing by 2.5 quintillion bytes per day, the amount of useful information almost certainly isn’t. Most of it is just noise, and the noise is increasing faster than the signal.

we can never make perfectly objective predictions. They will always be tainted by our subjective point of view.

The most practical definition of a Bayesian prior might simply be the odds at which you are willing to place a bet.

When this happened, Campbell had to ask the age-old programmer’s question: was the new move a feature of the program—a eureka moment that indicated it was growing yet more skilled? Or was it a bug?

My general advice, in the broader context of forecasting, is to lean heavily toward the “bug” interpretation when your model produces an unexpected or hard-to-explain result. It is too easy to mistake noise for a signal.

Computers are most useful to forecasters, therefore, in fields like weather forecasting and chess where the system abides by relatively simple and well-understood laws, but where the equations that govern the system must be solved many times over in order to produce a good forecast.

Poker is a pretty easy game if you know what cards your opponent holds.

The Pareto Principle of Prediction implies that the worst forecasters—those who aren’t getting even the first 20 percent right—are much worse than the best forecasters are good.

Presuming you are a betting man as I am, what good is a prediction if you aren’t willing to put money on it?

The heuristic of “follow the crowd, especially when you don’t know any better” usually works pretty well.

“The market can stay irrational longer than you can stay solvent.”

Like a baseball umpire, an intelligence analyst risks being blamed when something goes wrong but receives little notice when she does her job well.

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.

But our bias is to think we are better at prediction than we really are.