XLSTAT: Dixon test to detect outliers in Excel

This tutorial will help you run a Dixon test to detect outliers and interpret the results in Excel using XLSTAT. Not sure this is the statistical test you are looking for? Check out this guide .

Background

The Dixon test is a statistical method used to detect outliers, especially in small datasets. It can help to determine if an extreme value in a sample is an outlier and should be removed.

Dataset for Example

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.

Setting Up

• Open XLSTAT.
• Click on the Testing outliers menu and select the Dixon test feature:

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

In the Options tab:

• Select the two-sided alternative hypothesis to test for any significant association in either direction.
• Keep the significance level at the default value of 5%.
• Choose the Monte Carlo simulation method to estimate the p-value.
• Set the number of simulations to 1,000,000 for high precision in the results.

• Click on the OK button to launch the computations.
• The results are displayed on a new Excel sheet.

Interpreting the Results

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 Z-score table, the outliers are displayed in bold.

 

Finally, the Z-scores are displayed in a bar chart.