Grubbs's test is based on the assumption of normality.
That is, one should first verify that the data can be reasonably approximated by a normal distribution before applying the Grubbs test.
However, multiple iterations change the probabilities of detection, and the test should not be used for sample sizes of six or fewer since it frequently tags most of the points as outliers.
This is the two-sided test, for which the hypothesis of no outliers is rejected at significance level α if with tα/(2N),N−2 denoting the upper critical value of the t-distribution with N − 2 degrees of freedom and a significance level of α/(2N).
This article incorporates public domain material from the National Institute of Standards and Technology