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Mann-Whitney U test using Minitab


The Mann-Whitney U test determines whether there is a statistically significant difference between two unrelated, independent groups on a dependent variable.

For example, you could use a Mann-Whitney U test to determine whether there is a difference in test anxiety between undergraduate and postgraduate students (i.e., the dependent variable would be "test anxiety", and the independent variable would be "educational level", which has two groups: "undergraduate students" and "postgraduate students"). Alternately, you could use a Mann-Whitney U test to determine whether there is a difference in typing speed based on room lighting (i.e., the dependent variable would be "typing speed" and the independent variable would be "room lighting", which has two groups: "red lighting" and "blue lighting").

In this guide, we show you how to carry out a Mann-Whitney U test using Minitab, as well as interpret and report the results from this test. However, before we introduce you to this procedure, you need to understand the different assumptions that your data must meet in order for a Mann-Whitney U test to give you a valid result. We discuss these assumptions next.



The Mann-Whitney U test has four "assumptions". You cannot test the first three of these assumptions with Minitab because they relate to your study design and choice of variables. However, you should check whether your study meets these three assumptions before moving on. If these assumptions are not met, there is likely to be a different statistical test that you can use instead. Assumptions #1, #2 and #3 are explained below:

Assumption #4 relates to the nature of your data and can be checked using Minitab. You have to check that your data meets this assumption because if it does not, the results you get when running a Mann-Whitney U test might not be valid. In fact, do not be surprised if your data violates this assumption. This is not uncommon. However, there are possible solutions such that you can still use a Mann-Whitney U test. Assumption #4 is explained below:

In practice, checking for assumption #4 will probably take up most of your time when carrying out a Mann-Whitney U test. However, it is not a difficult task, and Minitab provides all the tools you need to do this.

In the section, Test Procedure in Minitab, we illustrate the Minitab procedure required to perform a Mann-Whitney U test assuming that no assumptions have been violated. First, we set out the example we use to explain the Mann-Whitney U test procedure in Minitab.



A company commissions an Advertising Agency to create a TV advert to promote a new product. Since the product is designed for men and women, the TV advert has to appeal to men and women equally. Before the company spends $250,000 to run the advert across a number of TV networks, it wants to make sure that it appeals equally to men and women. More specifically, the company wants to know whether the way that men and women "engage" with the TV advert is the same.

To achieve this, the TV advert is shown to 20 men and 20 women, who are then asked to fill in a questionnaire that measures their engagement with the advertisement. The questionnaire provides an overall engagement score. This overall engagement score is the dependent variable, which we have labelled Engagement in Minitab. Our independent variable, which we have labelled Gender in Minitab, contains two groups: "Males" and "Females".

An independent t-test was used to determine whether there was a statistically significant difference in mean engagement between males and females. Since the Advertising Agency needs the advertisement to be similarly engaging, they hope there is no difference!


Setup in Minitab

In Minitab, we set up the two related groups as though they were two variables. Therefore, under column we entered the name of the first related group, Female engagement, as follows: . Then, under column we entered the name of the second related group, Male engagement, as follows: . Finally, we entered the scores on the dependent variable for each of the two independent groups (i.e., the engagement score for "females" in the Female engagement column and the engagement scores for "males" in the Male engagement column). This is illustrated below:

Data setup for the Mann-Whitney U test in Minitab

Published with written permission from Minitab Inc.


Test Procedure in Minitab

In this section, we show you how to analyze your data using a Mann-Whitney U test in Minitab when the four assumptions in the previous section, Assumptions, have not been violated. Therefore, the three steps required to run a Mann-Whitney U test in Minitab are shown below:


Output of the Mann-Whitney U test in Minitab

The Minitab output for the Mann-Whitney U test is shown below. This output provides useful descriptive statistics for the two independent groups that were compared, including the sample size and median, as well as actual results from the Mann-Whitney U test.

Output for the Mann-Whitney U test in Minitab

Looking at the "Median" column, you can see that median engagement scores were higher for males (the Male engagement row) compared to females (the Female engagement row). The median difference in engagement between the two groups (i.e., males and females) was -0.1925 (the Point estimate for ETA1-ETA2 row) with 95% confidence intervals (95% CI) for the median difference in engagement of -0.420 to 0.055 (the 95.0 Percent CI for ETA1-ETA2 row) (ETA is the name given to the median). You are also presented with the Wilcoxon test statistic, W (W) of 355.0 and the statistical significance (2-tailed p-value) of this test (the Test of ETA1 = ETA2 vs ETA1 not = ETA2 is significant row), which is 0.1404 (the p-value is adjusted for ties and is equivalent to the Mann-Whitney U test). As the p-value is greater than 0.05 (i.e., p > .05), it can be concluded that there isn't a statistically significant difference in median engagement between our two groups: males and females. In other words, the median difference in engagement between males and females is not statistically significantly different l to zero.

Note: We present the output from the Mann-Whitney U test above. However, since you should have tested your data for the assumptions we explained earlier in the Assumptions section, you will also need to interpret the Minitab output that was produced when you tested for them. This includes the histograms that are produced to determine whether the two distributions of your independent variable (e.g., the distributions for "males" and "females") have the same shape. Also, remember that if your data failed any of these assumptions, the output that you get from the Mann-Whitney U test procedure (i.e., the output we discuss above) might no longer be valid.


Reporting the output of the Mann-Whitney U test

When you report the output of your Mann-Whitney U test, it is good practice to include:

Based on the Minitab output above, we could report the results of this study as follows:

A Mann-Whitney U test was run on 40 participants to determine if there were differences in engagement score between males and females. Median engagement score for males (5.58) and females (5.38) was not statistically significantly different, p = .140.

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