Outliers

An outlier is a value that doesn't fit the pattern of the data around it — sometimes a one-off mistake, sometimes a sign something systematic is going on. The examples below are deliberately obvious, but real outliers usually aren't, which is why there are quantitative tests for how likely a point actually is one: a value beyond the 1.5× IQR whiskers, more than 2-3 standard deviations from the mean, unusually far from a fitted trend line, or — for skewed data where the mean and standard deviation are themselves thrown off by outliers — a modified z-score built from the median instead. This page has two examples of how a single unusual value can distort an analysis, and what changes once you flag it.

Pick a tab below to try one.

What this teaches: Move points and compare outlier tests, including the 1.5× IQR fences and distance from the mean in standard deviations, on scatter and time-series data. It teaches how outlier detection works, why different tests flag different points, and how a single outlier can distort the mean and a trend line.