Some Finishing Thoughts From The Signal And The Noise By Nate Silver

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These are some quotes from the book which I wanted to share in which I feel are important for each of us who make prediction or forecast to explore:

From page 183:

“Getting feedback about how well our predictions have done is one way—perhaps the essential way—to improve them. Economic forecasters get more feedback that people in most other professions, but they haven’t chosen to correct for their bias toward overconfidence.”

From page 191:

“A forecaster should almost never ignore data, especially when she is studying rare events like recessions or presidential elections, about which there isn’t very much data to begin with. Ignoring data is often a tip-off that the forecaster is overconfident, or is overfitting her model—that she is interested in showing off rather than trying to be accurate.”

I encourage our readers to tackle chapter 8 entitled Less And Less And Less Wrong which gives a great introduction and analysis of Baye’s Theorem and how to use it in everyday analysis for all types of disciplines.

From page 267:

“The need for prediction arises not necessarily because the world itself is uncertain, but because understanding it fully is beyond our capacity.”

From page 292:

“Nevertheless, a commitment to testing ourselves—actually seeing how well our predictions work in the real world rather than in the comfort of a statistical model—is probably the best way to accelerate the learning process.”

From page 307:

“As Arthur Conan Doyle once said, “Once you eliminate the impossible, whatever remains, no matter how improbable, must be the truth.” This is sound logic, but we have a lot of trouble distinguishing the impossible from the highly improbable and sometimes get in trouble when we try to make too fine a distinction.”

From page 388:

“The goal of any predictive model is to capture as much signal as possible and as little noise as possible. Striking the right balance is not always so easy, and our ability to do so will be dictated by the strength of the theory and the quality and quantity of the data. In economic forecasting, the data is very poor and theory is weak, hence Armstrong’s argument that “the more complex you make the model the worse the forecast gets.”

From page 448:

“This book is less about what we know than about the difference between what we know and what we think we know. It recommends a strategy so that we might close that gap. The strategy requires one giant leap and then some small steps forward. The leap is into the Bayesian way of thinking about prediction and probability.”

These are just several thoughts in which are readers may digest, but I would suggest whole-heartily that you read it inspectionally and then turn around and read it analytically.


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