In statistics, Grubbs's test or the Grubbs test (named after Frank E. Grubbs, who published the test in 1950[1]), also known as the maximum normalized residual test or extreme studentized deviate test, is a test used to detect outliers in a univariate data set assumed to come from a normally distributed population.
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.[2]
Grubbs's test detects one outlier at a time. This outlier is expunged from the dataset and the test is iterated until no outliers are detected. 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.[3]
Grubbs's test is defined for the following hypotheses:
H0: There are no outliers in the data set
Ha: There is exactly one outlier in the data set
The Grubbs test statistic is defined as
G=
| \displaystylemaxi=1,\ldots,\left\vertYi-\bar{Y | |
| \right\vert}{s} |
\overline{Y}
s
This is the two-sided test, for which the hypothesis of no outliers is rejected at significance level α if
G>
| N-1 | |
| \sqrt{N |
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).
Grubbs's test can also be defined as a one-sided test, replacing α/(2N) with α/N. To test whether the minimum value is an outlier, the test statistic is
G=
| \bar{Y | |
| -Y |
min}{s}
G=
| Ymax-\bar{Y | |
Several graphical techniques can be used to detect outliers. A simple run sequence plot, a box plot, or a histogram should show any obviously outlying points. A normal probability plot may also be useful.