Distinguishing the signal from the noise requires both scientific knowledge and self-knowledge: the serenity to accept the things we cannot predict, the courage to predict the things we can, and the wisdom to know the difference.

Distinguishing the signal from the noise requires both scientific knowledge and self-knowledge: the serenity to accept the things we cannot predict, the courage to predict the things we can, and the wisdom to know the difference.

Nate Silver
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First of all, I think it's odd that people who cover politics wouldn't have any political views.

Nate Silver
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Distinguishing the signal from the noise requires both scientific knowledge and self-knowledge.

Nate Silver
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We're not that much smarter than we used to be, even though we have much more information - and that means the real skill now is learning how to pick out the useful information from all this noise.

Nate Silver
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By playing games you can artificially speed up your learning curve to develop the right kind of thought processes.

Nate Silver
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...the ratings agencies' problem was in being unable or uninterested in appreciating the distinction between risk and uncertainty.

Nate Silver
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In science, progress is possible. In fact, if one believes in Bayes' theorem, scientific progress is inevitable as predictions are made and as beliefs are tested and refined.

Nate Silver
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Under Bayes' theorem, no theory is perfect. Rather, it is a work in progress, always subject to further refinement and testing.

Nate Silver
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Distinguishing the signal from the noise requires both scientific knowledge and self-knowledge: the serenity to accept the things we cannot predict, the courage to predict the things we can, and the wisdom to know the difference.

Nate Silver, The Signal and the Noise: Why So Many Predictions Fail - But Some Don't
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Who needs theory when you have so much information? But this is categorically the wrong attitude to take toward forecasting, especially in a field like economics where the data is so noisy.

Nate Silver, The Signal and the Noise: Why So Many Predictions Fail - But Some Don't
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