Quotes About Accuracy

But groups also let people share information and perspectives. That's good. It helps make dragonfly eye work, and aggregation is critical to accuracy.

on average, teams were 23% more accurate than individuals.

My research collaborator Don Moore points out that police officers spend a lot of time figuring out who is telling the truth and who is lying, but research has found they aren't nearly as good at it as they think they are and they tend not to get better with experience. That's because experience isn't enough. It must be accompanied by clear feedback.

Predictably, psychologists who test police officers' ability to spot lies in a controlled setting find a big gap between their confidence and their skill. And that gap grows as officers become more experienced and they assume, not unreasonably, that their experience has made them better lie detectors. As a result, officers grow confident faster than they grow accurate, meaning they grow increasingly overconfident.

Research on calibration—how closely your confidence matches your accuracy—routinely finds people are too confident.10 But overconfidence is not an immutable law of human nature. Meteorologists generally do not suffer from it. Neither do seasoned bridge players. That's because both get clear, prompt feedback.

So finding meaning in events is positively correlated with wellbeing but negatively correlated with foresight. That sets up a depressing possibility: Is misery the price of accuracy?

effective learning from experience can't happen without clear feedback, and you can't have clear feedback unless your forecasts are unambiguous and scorable. Sound familiar? It should. Drezner cited an article about the IARPA tournament.

If we are serious about measuring and improving, this won't do. Forecasts must have clearly defined terms and timelines. They must use numbers. And one more thing is essential: we must have lots of forecasts.

It was the earliest demonstration of a phenomenon popularized by—and now named for—James Surowiecki's bestseller The Wisdom of Crowds. Aggregating the judgment of many consistently beats the accuracy of the average member of the group, and is often as startlingly accurate as Galton's weight-guessers.

Researchers have found that merely asking people to assume their initial judgment is wrong, to seriously consider why that might be, and then make another judgment, produces a second estimate which, when combined with the first, improves accuracy almost as much as getting a second estimate from another person.

Our lives sometimes depend on computers performing as predicted.

Forecasters who see illusory correlations and assume that moral and cognitive weakness run together will fail when we need them most.

More often forecasts are made and then…nothing. Accuracy is seldom determined after the fact and is almost never done with sufficient regularity and rigor that conclusions can be drawn. The reason? Mostly it's a demand-side problem: The consumers of forecasting—governments, business, and the public—don't demand evidence of accuracy. So there is no measurement. Which means no revision. And without revision, there can be no improvement.

produce forecast-wrecking

Accurate forecasts may help do that sometimes, and when they do accuracy is welcome, but it is pushed aside if that's what the pursuit of power requires.

two hundred studies—has shown that in most cases statistical algorithms beat subjective judgment, and in the handful of studies where they don't, they usually tie. Given that algorithms are quick and cheap, unlike subjective judgment, a tie supports using the algorithm. The point is now indisputable: when you have a well-validated statistical algorithm, use it

in most cases statistical algorithms beat subjective judgment, and in the handful of studies where they don't, they usually tie. Given

The point is now indisputable: when you have a well-validated statistical algorithm, use it.

we are serious about measuring and improving, this won't do. Forecasts must have clearly defined terms and timelines. They must use numbers. And one more thing is essential: we must have lots of forecasts.

the more famous an expert was, the less accurate he was.

À ceux-là, en général, j'explique que la vraisemblance importe plus que la vérité, que la justesse compte davantage que l'exactitude et surtout qu'un lieu, ce n'est pas une topographie mais la manière dont on le raconte, pas une photographie mais une sensation, une impression.

For so many years, I was watching my tee shots slide hard to the right. I used to think I was hitting a draw at times, and the ball was still curving to the right! I still prefer to play a little fade, but I've had to recalibrate my visuals.

A good kitchen scale is an absolute must-have for every kitchen. Although nothing can substitute for skilled intuition - add a dash of this and a handful of that, cook until golden brown and delicious - there are times when the precision a scale can provide is vital.

If memories were indeed like what a camera records, they could be forgotten, or they could fade so that they are no longer clear and vivid. But it would be difficult to explain how people could have memories that are both clear and vivid while also being wrong. Yet that happens, and it is not infrequent.