Robust statistics are
statistics with good performance for data drawn from a wide range of
probability distributions, especially for distributions that are not
normal. Robust
statistical methods have been developed for many common problems, such as estimating
location,
scale and
regression parameters. One motivation is to produce
statistical methods that are not unduly affected by
outliers. Another motivation is to provide methods with good performance when there are small departures from parametric distributions. For example, robust methods work well for mixtures of two normal distributions with different standard-deviations; under this model, non-robust methods like a t-test work badly.