Running a Dixon test to detect outliers with XLSTAT
Dataset for testing outliers with Dixon test
An Excel sheet with both the data and the results can be downloaded by clicking here.
The data have been obtained from a normal distribution with mean 0 and variance 3. One outlier has been added. We wish to test if there is one outlier in the sample.
Goal of this tutorial
We would like to detect one or two outliers from a sample using Dixon test. You can find some detail on the test here.
Setting up a Dixon test to detect an outlier
To start the Dixontest go to the menu Testing outliers / Dixon test.
In the General tab, select the data and the automatic option.
As an alternative hypothesis choose the two-sided option. The default significance level is left as is: 5%. The p-value is obtained with a Monte Carlo simulation approach. We choose to use 1000000 simulations.
When ready click on OK.
Results of a test for detecting an outlier
The result is that the p-value for this test is smaller than 0.0001. That means that the null hypothesis should be rejected.
In the following table, the detected outliers are given.
You can also find the Z score to detect outliers in the output of this test.
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