Pareto, Power Laws, and Fat Tails
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In this video, I give a beginner-friendly guide to Power Laws and describe 3 problems with using traditional statistical methods to analyze them.
📹 Detecting Power Laws: • Detecting Power Laws in Real-world Data | ...
📹 Fat Tails: • 4 Ways to Measure Fat Tails with Python (+...
📰 Read more: https://medium.com/towards-data-scien...
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References
[1] Pareto principle. (2023, October 30). In Wikipedia. https://en.wikipedia.org/wiki/Pareto_...
[2] arXiv:2001.10488 [stat.OT]
[3] Taleb, N.N. (2007). The Black Swan: the impact of the highly improbable. New York; Random House.
[4] https://www.archives.gov/exhibits/inf...
[5] arXiv:0706.1062 [physics.data-an]
[6] Taleb, N. N. (2019). How much data do you need? An operational, pre-asymptotic metric for fat-tailedness. International Journal of Forecasting, 35(2), 677–686. https://doi.org/10.1016/j.ijforecast....
You can find many great lectures on this topic here: @nntalebproba
Intro - 0:00
Outline - 0:45
The Gaussian Distribution - 1:21
The Pareto Distribution - 2:42
Power Laws - 4:30
Mediocristan vs Extremistan - 5:31
3 Problems with STAT 101 - 8:44
Problem 1: The Mean is Meaningless - 10:07
Problem 2: Regression Doesn't Work - 14:30
Problem 3: Payoffs Diverge from Probabilities - 17:41
Controversy in Extremistan - 20:00
Fat Tails - 21:17
Takeaways - 24:38