Outliers in Statistics: How to Find and Deal with Them in Your Data
Outliers are unusually high or low observations that sit far from the rest of a dataset. In A/B testing, a small number of extreme values can materially change averages, variance, confidence intervals, and the conclusion you draw from a test.
This matters particularly for revenue metrics such as average order value and revenue per visitor, where a handful of unusually large purchases can have a disproportionate effect.
An outlier is not automatically bad data. It might be a recording error, unusual customer behavior, or a genuinely valuable segment. The important part is identifying it, understanding why it occurred, and deciding how it should be handled before it distorts your analysis.
