The video from my PyData Global 2023 talk, Extremes, outliers, and GOATS, is available now:

The slides are here.

There are two Jupyter notebooks that contain the analysis I presented:

• Chapter 4 of Probably Overthinking It
• An exploration of Gaussian and lognormal models for anthropomorphic measurements

Here’s the abstract:

The fastest runners are much faster than we expect from a Gaussian distribution, and the best chess players are much better. In almost every field of human endeavor, there are outliers who stand out even among the most talented people in the world. Where do they come from?

In this talk, I present as possible explanations two data-generating processes that yield lognormal distributions, and show that these models describe many real-world scenarios in natural and social sciences, engineering, and business. And I suggest methods — using SciPy tools — for identifying these distributions, estimating their parameters, and generating predictions.

This talk is based on Chapter 4 of Probably Overthinking It. If you liked the talk, you’ll love the book 🙂

Thanks to the organizers of PyData Global and NumFOCUS!