Allen Downey is a curriculum designer at the online learning company Brilliant and professor emeritus at Olin College. He is the author of several books related to computer science and data science, including Think Python, Think Stats, and Think Bayes. His blog, Probably Overthinking It, features articles about Bayesian statistics. He received his Ph.D. in Computer Science from U.C. Berkeley, and M.S. and B.S. degrees from MIT.
Long-tailed distributions are common in natural and engineered systems; as a result, we encounter extreme values more often than we would expect from a short-tailed distribution. If we are not prepared for these "black swans", they can be disastrous.
But we have statistical tools for identifying long-tailed distributions, estimating their parameters, and making better predictions about rare events.
In this talk, I present evidence of long-tailed distributions in a variety of datasets -- including earthquakes, asteroids, and stock market crashes -- discuss statistical methods for dealing with them, and show implementations using scientific Python libraries.