<Li> ASTM E178 Standard Practice for Dealing With Outlying Observations </Li> <Li> Mahalanobis distance and leverage are often used to detect outliers, especially in the development of linear regression models . </Li> <Li> Subspace and correlation based techniques for high - dimensional numerical data </Li> <P> It is proposed to determine in a series of m (\ displaystyle m) observations the limit of error, beyond which all observations involving so great an error may be rejected, provided there are as many as n (\ displaystyle n) such observations . The principle upon which it is proposed to solve this problem is, that the proposed observations should be rejected when the probability of the system of errors obtained by retaining them is less than that of the system of errors obtained by their rejection multiplied by the probability of making so many, and no more, abnormal observations . (Quoted in the editorial note on page 516 to Peirce (1982 edition) from A Manual of Astronomy 2: 558 by Chauvenet .) </P>

Which of the following is a possible reason for an outlier in a data set