Chauvenet's criterion

To apply Chauvenet's criterion, first calculate the mean and standard deviation of the observed data.

It was developed a few years before Chauvenet's criterion was published, and it is a more rigorous approach to the rational deletion of outlier data.

In statistical theory, Chauvenet's criterion (named for William Chauvenet) is a means of assessing whether one piece of experimental data - an outlier - from a set of observations, is likely to be spurious.

Deletion of outlier data is a controversial practice frowned on by many scientists and science instructors; while Chauvenet's criterion provides an objective and quantitative 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.