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That's true but those people were sampling closer to once per day, and still got N-sigma in their models for some high value of N.

The easiest way to get something that's not actually normal in the typical central limit theorem construction is to have the variables be just slightly not independent. Which becomes obvious in retrospect after you start realizing the dependence and shit hits the fan.

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Banks failing and CDOs exploding would be two good examples; they’re rare if not nonexistent but if they start they may all flare up.

Also natural distribution assumes you don’t have someone trying to game the extremes to make money. If being seven feet tall resulted in billions we’d have people researching how to grow taller people.

You can see stuff like this in distributions of tax returns - many people will be unexpectedly clustered right below certain cut-off amounts.

Worse is correlation break-down in a sense - e.g., that black swan event happening across an entire asset class because of liquidity squeeze, margin calls, etc.

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