Nassim Taleb’s Probability Moocs: The empirical distribution is … not empirical

For fat tailed distributions, the empirical distributions does not reflect the true statistical properties, particularly for extremes. This is a simplified side note to a paper with Mark Spitznagel on why people make a mistake by looking at raw historical data as “empiricism”.

So I am fed up with academics who say “we know it is fat tails” yet not understand the consequences.

Randomness of Correlation & Its Hacking by Big Dataists


This tutorial presents the intuitions of the randomness of sample correlation (spurious correlation) and the methodologies in derivations. Some later sections are somewhat technical as Nassim rederived an old equation with more precise functions (in order to apply to fat tails) and showed the distribution of the maximum of d variables with n points per variable.
This paves the way to the real scientific work on random matric theory under fat tails and the failure of Marchenko-Pastur.

A Mini-MOOC Tutorial: The Randomness of Correlation and its Hacking by Big Dataists

Nassim says:

This tutorial presents the intuitions of the randomness of sample correlation (spurious correlation) and the methodologies in derivations.
Some later sections are somewhat technical as rederived an old equation with more precise functions (in order to apply to fat tails) and showed the distribution of the maximum of d variables with n points per variable.
This paves the way to the real scientific work on random matric theory under fat tails and failure of Marchenko-Pastur.

Taleb’s MOOCs | Binary vs Vanilla Payoffs and Predictions: An error in the research/risk literature

“Micro-Mooc on a paper by Taleb and Tetlock (one manifestation of the LUDIC FALLACY). There are serious statistical differences between predictions, bets, and exposures that have a yes/no type of payoff, the “binaries”, and those that have varying payoffs, which we call the “vanilla”. Real world exposures tend to belong to the vanilla category, and are poorly captured by binaries. Yet much of the economics and decision making literature confuses the two. Vanilla exposures are sensitive to Black Swan effects, model errors, and prediction problems, while the binaries are largely immune to them. The binaries are mathematically tractable, while the vanilla are much less so. Hedging vanilla exposures with binary bets can be disastrous–and because of the human tendency to engage in attribute substitution when confronted by difficult questions,decision-makers and researchers often confuse the vanilla for the binary.”
The paper is here: http :// papers. ssrn. com/ sol3/ papers.cfm? abstract_id= 2284964
More general Fat Problems with Tails: http:// www. fooled by randomness. com/ FatTails. html