"A forecaster should almost never ignore data, especially
when she is studying rare events […]. 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."
"Complex systems seem to have this property, with large
periods of apparent stasis marked by sudden and catastrophic failures. These
processes may not literally be random, but they are so irreducibly complex
(right down to the last grain of sand) that it just won’t be possible to
predict them beyond a certain level. […] And yet complex processes produce
order and beauty when you zoom out and look at them from enough distance."
"Data-driven predictions can succeed-and they can fail. It is when we deny our role in the process that the odds of failure rise. Before we demand more of our data, we need to demand more of ourselves." (Nate Silver, "The Signal and the Noise: Why So Many Predictions Fail-but Some Don't", 2012)
"Distinguishing the signal from the noise requires both scientific knowledge and self-knowledge." (Nate Silver, "The Signal and the Noise: Why So Many Predictions Fail-but Some Don't", 2012)
"Finding patterns is easy in any kind of data-rich environment; that's what mediocre gamblers do. The key is in determining whether the patterns represent signal or noise." (Nate Silver, "The Signal and the Noise: Why So Many Predictions Fail-but Some Don't", 2012)
"The instinctual shortcut that we take when we have 'too much information' is to engage with it selectively, picking out the parts we like and ignoring the remainder, making allies with those who have made the same choices and enemies of the rest." (Nate Silver, "The Signal and the Noise: Why So Many Predictions Fail-but Some Don't", 2012)
"The most basic tenet of chaos theory is that a small change in initial conditions - a butterfly flapping its wings in Brazil - can produce a large and unexpected divergence in outcomes - a tornado in Texas. This does not mean that the behavior of the system is random, as the term 'chaos' might seem to imply. Nor is chaos theory some modern recitation of Murphy’s Law ('whatever can go wrong will go wrong'). It just means that certain types of systems are very hard to predict." (Nate Silver, "The Signal and the Noise: Why So Many Predictions Fail-but Some Don't", 2012)
"The signal is the truth. The noise is what distracts us from the truth." (Nate Silver, "The Signal and the Noise: Why So Many Predictions Fail-but Some Don't", 2012)
"The systems are dynamic, meaning that the behavior of the system at one point in time influences its behavior in the future; And they are nonlinear, meaning they abide by exponential rather than additive relationships." (Nate Silver, "The Signal and the Noise: Why So Many Predictions Fail-but Some Don't", 2012)
"We forget - or we willfully ignore - that our models are simplifications of the world. We figure that if we make a mistake, it will be at the margin. In complex systems, however, mistakes are not measured in degrees but in whole orders of magnitude." (Nate Silver, "The Signal and the Noise: Why So Many Predictions Fail-but Some Don't", 2012)
"We need to stop, and admit it: we have a prediction problem. We love to predict things—and we aren't very good at it." (Nate Silver, "The Signal and the Noise: Why So Many Predictions Fail-but Some Don't", 2012)
"Whether information comes in a quantitative or qualitative
flavor is not as important as how you use it. [...] The key to making a good forecast […] is not in limiting
yourself to quantitative information. Rather, it’s having a good process for
weighing the information appropriately. […] collect as much information as
possible, but then be as rigorous and disciplined as possible when analyzing
it. [...] Many times, in fact, it is possible to translate qualitative
information into quantitative information."
"Statistics is the science of finding relationships and actionable insights from data." (Nate Silver)
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