In statistics such as stratified samples, an<unk>  is an observation that is numerically distant from the rest of the data. Statistics derived from data sets that include<unk> s will often be misleading. For example, if one is calculating the average temperature of 10 objects in a room, and most are between 20-25° Celsius, but an oven is at 350° C, the median of the data may be 23 but the mean temperature will be 55.  In this case, the median better reflects the temperature of a randomly sampled object than the mean.<unk> s may be indicative of data points that belong to a different population than the rest of the sample set.
In most samplings of data, some data points will be further away from their expected values than what is deemed reasonable. This can be due to systematic error, faults in the theory that generated the expected values, or it can simply be the case that some observations happen to be a long way from the center of the data.<unk>  points can therefore indicate faulty data, erroneous procedures, or areas where a certain theory might not be valid. However, a small number of<unk> s is expected in normal distributions.
Estimators not sensitive to<unk> s are said to be robust.
Deletion of<unk>  data is a controversial practice frowned on by many scientists and science instructors; while mathematical criteria provides an objective and quantitiative method for data rejection, it does not make the practice more scientifically or methodologically sound, especially in small sets or where a normal distribution cannot be assumed. Rejection of<unk> s is more acceptable in areas of practice where the underlying model of the process being measured and the usual distribution of measurement error are confidently known.  When practiced, rejection of<unk> s usually is based on some rule such as the quartile rules given below, Chauvenet's Criterion, or Grubbs Test.
Outlier
Special characters
Mosaic 
Grief
Complex 
Blunt 
Burial
Imogene King