In a dataset, a value that falls notably farther from the center than almost all of the other values is called an outlier. Whether a particular value should be considered an outlier depends on the distribution and sample size. In general, except in very large samples, one would be likely to treat any value that falls more than three standard deviations from the mean as an outlier.

Outliers have no effect on the median or mode, but can significantly distort the mean. A commonly cited example is that if a group of math teachers is joined by a billionaire, then each of them becomes wealthy—on average.

Outliers can be caused by variability in the individuals being measured, or by measurement error. If measurement error is suspected, the outlier might best be removed from the dataset before it is analyzed further or any conclusions drawn.