Silver 2012 Penguin Press

Silver N (2012) The signal and the noise. The art and science of prediction. Penguin Press 534 pp.

Silver N (2012) Penguin Press

Abstract: Some quotes selectred throught this remarkable book:

• The story the data tell us is often the one we'd like to hear.
• We face a danger whenever information growth outpaces our understanding of how to process it. The last forty years of human history imply that it can still take a long time to translate information into useful knowledge.
• The signal is the truth. The noise is what distracts us from the truth.
• .. if the quantity of inofrmation 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.
• .. a certain amount of immersion in a topic will provide disproportionately more insight than an executive summary.
• We think we want information when we really want knowledge.
• The signal is the truth. The noise is what distracts us from the truth.

Labels:

Organism: Human 

Quotes continued

• We focus on those signals that tell a story about the world as we would like it to be, not how it really is.
• .. even if the amount of knowledge in the world is increasing, the gap between what we know and what we think we know may be widening. This syndrome is often associated with very precise-seeming predictions that are not at all accurate.
• The more interviews that an expert had done with the press, Tetlock found, the ‘’worse’’ his predictions tended to be.
• Our brains, wired to detect patterns, are always looking for a signal, when instead we should appreciate how noisy the data is.
• Wherever there is human judgement there is the potential for bias. The way to become more objective is to recognize the influence that our assumptions play in our forecasts and to question ourselves about them.
• Good innovators typically think very big ‘’and’’ they think very small.
• - a visual inspection of a graphic showing the interaction between two variable is often a quicker and more reliable way to detect outliers in your data than a statistical test.
• One of the most important tests of a forecast - - is called ‘’calibration’’.
• When catastrophe strikes, we look for a signal in the noise.
• If you’re speaking with a seismologist:

1. A ‘’’prediction’’’ is a definitive and specific statement about when and where an earthquake will strike ..
2. Whereas a ‘’’forecast’’’ is a probabilistic statement, usually over a longer time scale ..

The USGS’s official position is that earthquakes cannot be predicted. They can, however, be ‘’forecasted’’.

• What happens in systems with noisy data and underdeveloped theory - - is a two-step process. First, people start to mistake the noise for a signal. Second, this noise pollutes journals, blogs, and news accounts with false alarms, undermining good science and setting back our ability to understand how the system really works.
• In statistics, the name given to the act of mistaking noise for a signal is ‘’overfitting’’.
• Overfitting represents a double whammy: it makes our model look ‘’better’’ on paper but perform ‘’worse’’ in the real world. .. This may make it easier to get the model published in an academic journal or to sell to a client, crowding out more honest models from the marketplace. But if the model is fitting noise, it has the potential to hurt the science.
• As Hatzius sees it, economic forecasters face three fundamental challenges. First, it is very hard to determine cause and effect from economic statistics alone. Second, the economy is always changing, so explanations of economic behavior that hold in one business cycle may not apply to future ones. And third, as bad as their forecasts have been, the data that economists have to work with isn’t much good either.