![]() ![]() It is extremely common to find data scientists, academics and even some statisticians who misinterpret p-values. The p-value is the probability of observing data as extreme or more than ours if the null hypothesis is true : As some traders say, “fat-tails hide their tails”. The most insidious aspect of fat-tailed variables is that they will appear Gaussian until the advent of a catastrophic, extreme event. ![]() Fat-tailed distributions resemble a Gaussian (Normal) distribution, except the probability of extreme events is much greater and does not decrease exponentially as we get further from the mean, but sub-exponentially. īankers, policy makers, economists and other forecasting consumers all still rely heavily to this day on Gaussian assumptions which have been shown to be false. It undermines nearly ALL attempts are forecasting, particularly in complex dynamical systems. I put this statistical fallacy in first place, even though the rest of this list is in no particular order, because I believe it has the most devastating consequences. This was discovered by Benoit Mandelbrot in 1962. Yet most financial and econometric models assume a Gaussian distribution. Most socio-economic variables usually follow fat-tailed distributions, not Gaussian ones.
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