Steven Sanderson looks for oddities:
Outliers can significantly skew your data analysis results, leading to inaccurate conclusions. For R programmers, effectively identifying and removing outliers is crucial for maintaining data integrity. This guide will walk you through various methods to handle outliers in R, focusing on multiple columns, using a synthetic dataset for demonstration.
The techniques Steven uses are perfectly reasonable (though I like to use MAD from the median rather than standard deviations from the mean because MAD from the median doesn’t suffer from the sorts of endogeneity problems standard deviation does in a dynamic process). My primary warning would be to keep outliers in a dataset unless you know why you’re removing them. If you know the values were impossible or wrong—for example, a person who works 500 hours a week—that’s one thing. But sometimes, you get exceptional values out of an ordinary process, and those values are just as real as any other. I might have had a sequence in which I flipped a fair coin and it landed on heads 10 times in a row. It’s statistically very uncommon, but that doesn’t mean you can ignore it as a possibility or pretend it didn’t happen.