The Signal and the Noise: Why So Many Predictions Fail – But Some Don’t by Nate Silver

Nate silver writes on his blog http://fivethirtyeight.com/ about sports and political forecasting.

Most economists try to predict too accurately and are too confident about their skills.

Every prediction always needs the proper assessment of a human being.

You can use Bayes’ theorem to account for errors in your own predictions.

Bayes Theorem – https://en.wikipedia.org/wiki/Bayes%27_theorem

describes the probability of an event, based on prior knowledge of conditions that might be related to the event. For example, if cancer is related to age, then, using Bayes’ theorem, a person’s age can be used to more accurately assess the probability that they have cancer, compared to the assessment of the probability of cancer made without knowledge of the person’s age.

We are not very good at predicting the future.

Biases decrease accuracy of forecasting. Sometimes the bias is from special interests.

Having a pre-existing narrative or political partisanship bias negatively impacts forecasting accuracy.

The author discussed several examples such as predicting the stock market, poker, weather, earthquakes and other natural phenomenon, and terrorist/economic/political events.

I feel the perfect back-to-back reading is to read with this book –

Superforecasting: The Art and Science of Prediction by Philip E. Tetlock, Dan Gardner

Also relevant is the book –

Ego Is the Enemy by Ryan Holiday

Like the author, Ryan Holiday mentions how the ego can  be ones downfall.

The signal is a metaphor for the correct data.

The noise is a metaphor for inaccurate data and other irrelevant information that misleads and causes predictions to fail.

Prediction is saying that a specific thing (usually with a level of severity) will happen at a specific time.

Forecasting is saying that an event has a statistical likelihood of occurrence within an approximate time frame.

Use common sense and human judgement in forecasting as well as math and statistics. Example is baseball scouts and statistical analysis of players performance.

Reference: Moneyball: The Art of Winning an Unfair Game by Michael Lewis

With the advent of the Internet and big data, the shear volume of data has increased exponentially. This makes it harder to separate the signal from the noise. The internet contains more data now but there’s no guarantee it is correct data.

People are by nature pattern seeking creatures. Often times we see patterns where there are none.

Causation vs. correlation

Occam’s razor – Among competing hypotheses, the one with the fewest assumptions should be selected, or, all things being equal, the simplest solution tends to be the correct one.

False positives are as dangerous if not more so than false negatives. For example, the odds of a test being wrong can be greater than the odds of having the condition to begin with. Example, getting cancer may be 1% while a false positive for having cancer may be 10%. This was actually seen with breast and prostate cancer.

The boy crying wolf syndrome where forecasting is ignored then something bad happens.

“The fox knows many little things, but the hedgehog knows one big thing”.

To predict the behavior of a system requires a thorough understanding of it. Weather, stock market, political predictions, earthquakes. These complex systems have so many moving parts, makes it nearly impossible to predict with perfection. The farther out in time one tries to predict, the less accurate the prediction becomes (and quickly).

Weather programs on TV predict on the “wetter” side because if they are wrong and you get wet, people are pissed while if they predict rain and you get sun the people are pleasantly surprised. There is bias in predictions, even weather.

earthquakes and terrorist attacks follow a power law distribution – smaller events occur more frequently and si