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Classical Methods for Outlier Detection

Vinod Chugani is speaking my language:

Outliers are unique in that they often don’t play by the rules. These data points, which significantly differ from the rest, can skew your analyses and make your predictive models less accurate. Although detecting outliers is critical, there is no universally agreed-upon method for doing so. While some advanced techniques like machine learning offer solutions, in this post, we will focus on the foundational Data Science methods that have been in use for decades.

Vinod looks at a few techniques, including inter-quartile range and comparing results to an expected distribution. If you’re really excited about this topic, I know a guy who’s written a bit about it.