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A statistical procedure is considered to be robust if it performs well even when required assumptions are not met, or if the procedure performs well for a large number of distributions. A "statistical procedure" could be any item, from an estimate to a statistical test, or from a modeling technique to cluster analysis. Robustness is a big issue in applied data analysis, since practical problems tend to create outliers.

A simple example of a robust statistic is the median in comparison with the mean. The median is less affected by outliers than the mean.

Last Update: 2004-Jul-03