A review by inquiry_from_an_anti_library
The Signal and the Noise: Why So Many Predictions Fail--But Some Don't by Nate Silver

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4.0

Is This An Overview?

Having a lot of information does not mean there is a lot of validity in the information.  There is difficulty in understanding large quantities of information, and difficult to differentiate between useful information from misinformation.  While people want useful information, want the Signal, much of the information is not useful, information that is noise.  Noise distracts people from the Signal.  The quality of predictions, or forecasts, depends on filtering the Signal from the Noise. 

 

The data, the evidence, the numbers do not represent themselves.  The evidence is represented by people, who tend to favor the evidence they want to hear.  Confirming their views which limits their decisions, and causes them to miss evidence that can affect the decisions being made.  People are biased, and therefore develop biased predictions.  To improve data-driven predictions, people need to improve their ability to sort the information. 

 

Prediction failures tend to have features in common such as focusing on what is wanted rather than what is, ignoring difficult to measure risks, making inappropriate approximations and assumptions, and misunderstanding uncertainty.  Forecasts tend to improve when people think of various alternative views, and update their views to new information. 

 

What Is Forecasting?

Models are a tool to represent the complexities of reality, they do not substitute for reality.  A prediction is a definite and specific statement of what might happen, while a forecast is a probabilistic statement of what might happen.  Risk is knowing what the options are, while uncertainty is not knowing the options or information that can affect the options. 

 

Systems which are dynamic and nonlinear (chaos theory), made predictions difficult.  People can change their behavior to a prediction, therefore changing the prediction itself into a self-fulfilling prediction as people support the claims or a failed prediction by avoiding the claims.

 

Good forecasts are those which over time make more correct predictions.  A Bayesian analysis is a method of updating beliefs, as the method gets the person closer and closer to the truth. 

 

Caveats?

Advice for how to improve decisions are described using examples, the advice is hidden within the examples.  The examples are noise that the reader needs to engage with to find the signal.   The value of the examples depends on the interests of the reader.  There is not much of a systematic analysis, a lack of a summary for the advice.