Observations often contain rogue **outlier** values that
lie far away from the bulk of the data.
These can be caused by measurement or recording errors or can be
due to genuine freak events. Especially when
dealing with small samples, outliers can bias the previous summary
statistics away from values representative for majority of the sample.

This problem can be avoided either by eliminating or downweighting the outlier
values in the sample (**quality control**), or by using statistics
that are **resistant** to the presence of outliers.
Note that the word **robust** should not be used to signify resistant since
it is used in statistics to refer to insensitivity to choice of probability
model or estimator rather than data value.
Because the range is based on the extreme minimum and maximum values in
the sample, it is a good example of a statistic that is not at all resistant
to the presence of an outlier (and so should be interpreted very carefully !).

David Stephenson 2005-09-30