**Observed variables often contain rogue outlier values that
lie far away from the sample mean. 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 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 !).
**

**Resistant summary statistics can be obtained by using
the sample quantiles (percentiles/fractiles).
Quantiles are constructed by sorting (ranking) the data into
ascending order to obtain a sequence of
order statistics **

**Unlike the arithmetic mean, the median is not at all influenced by
the exact value of the largest objects and so provides a resistant
measure of the central location.
Likewise, a resistant measure of the scale can be obtained using the
Inter-Quartile Range (IQR) given by the difference between the
upper and lower quartiles **