Why robust statistics?
1 Barnett and Lewis: "We will define an outlier in a set of data to be an observation (or subset of observations) that seems inconsistent with the rest of that data set." The Robust Statistics reduces incorrect measurement risks generated by outliers.
2In some applied fields, often are the so-called "outliers" which are of interest (fraud detection is an example). Sometimes, additionally, observations might cluster in such a way to become interesting for their own pattern, which with robust methods can be properly disentangled from the bulk of the data.
3It almost unavoidable, when analyzing big data, to avoid errors or outliers. With Robust Statistics the final results are not affected by those.
4The MATLABÂ® Toolbox FSDA "Flexible Statistics for Data Analysis Toolbox", developed by the Center, is a powerful tool to be used in data analysis; it us maintained and updated regularly with new tools.