4 Ways to Measure Fat Tails with Python (+ Example Code)

🤝 Want your team maximizing Claude? I run 1:1 and team AI workshops for companies doing $1M+ per year: https://aibuilder.academy/yt/15Kd9OPn7tw This is the 3rd video in a series about Power Laws and Fat Tails. In this video, I break down 4 ways we can quantify fat tails and share Python code analyzing real-world data. 📹 Series Intro: https://youtu.be/Wcqt49dXtm8 📹 Previous video: https://youtu.be/x5-IW1m3zPo 📰 Read more: https://medium.com/towards-data-science/4-ways-to-quantify-fat-tails-with-python-10ce62c0ada1?sk=3aa9397cdd9f8acbd0fdf40d90c2cba5 💻 GitHub Repo: https://github.com/ShawhinT/YouTube-Blog/tree/main/power-laws/3-quantifying-fat-tails References [1] Scipy Kurtosis: https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.kurtosis.html#scipy.stats.kurtosis [2] Scipy Moment: https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.moment.html [3] arXiv:1802.05495 [stat.ME] [4] https://en.wikipedia.org/wiki/Log-normal_distribution [5] Pham-Gia, T., & Hung, T. (2001). The mean and median absolute deviations. Mathematical and Computer Modelling, 34(7–8), 921–936. https://doi.org/10.1016/S0895-7177(01)00109-1 Intro - 0:00 Fat Tails - 0:45 4 Ways to Quantify Fat Tails - 2:02 Heuristic 1: Power Law Tail Index - 2:32 Heuristic 2: Kurtosis - 3:50 Heuristic 3: Log-normal's σ - 5:29 Heuristic 4: Taleb's κ - 7:04 Example Code: Quantifying Fat Tails in Social Media - 11:44 What's next? - 22:29