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.

doxon test menu

In the General tab, select the data and the automatic option.

dixon test dialog box general

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.

dixon test dialog box options

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.

dixon test result

In the following table, the detected outliers are given.

dixon test outlier

You can also find the Z score to detect outliers in the output of this test.

 

Click here for other tutorials.

About KCS

Kovach Computing Services (KCS) was founded in 1993 by Dr. Warren Kovach. The company specializes in the development and marketing of inexpensive and easy-to-use statistical software for scientists, as well as in data analysis consulting.

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