> Most statisticians would say that there's not really a difference between an outlier and a non-outlier, but actually the data just have fatter tails than you

> Most statisticians would say that there's not really a difference between an outlier and a non-outlier, but actually the data just have fatter tails than you originally thought, and you need to use a robust modeling method to account for that. No statisticians would ever propose that as a practical industrial method. Sometimes a bat falls in your vat of beer at the exact moment the lid is off and no sensible statistician proposes modelling the flight path of bats. Understanding and removal of outliers has been part of the statisticians toolbox forever. Gosset's[1] (The inventor of modern statistics) whole work on Student's t-distribution was because he had to deal with small sample size and know when sample weren't representative, and how to discard outliers. Studentized Residual is named for him and is (to quote Wikipedia)[2]: an important technique in the detection of outliers. The statisticians I've worked with are very pragmatic, and none want to try to model the whole universe. It was a statistician who came up with "all models are wrong but some are useful" after all. [1] https://en.wikipedia.org/wiki/William_Sealy_Gosset https://en.wikipedia.org/wiki/William_Sealy_Gosset [2] https://en.wikipedia.org/wiki/Studentized_residual https://en.wikipedia.org/wiki/Studentized_residual