Statistical Non Parametric Mann – Whitney U test

T. Dhasaratharaman*

Statistician, Kauvery Hospitals, India

*Correspondence: Tel.: +91 90037 84310; email: [email protected]

Non-parametric statistics are used when the data are not normally distributed and so are not appropriate for “parametric” tests.

Interpretation

Rather than comparing the values of the raw data, statisticians “rank” the data and compare the ranks.

Example

Mann-Whitney U Test

The Mann-Whitney U test is used to compare differences between two independent groups when the dependent variable is either ordinal or continuous, but not normally distributed.

A GP introduced a nurse triage system into her practice. She was interested in finding out whether the age of the patients attending for triage appointments was different to that of patients who made emergency appointments with the GP.

Six hundred and forty-six patients saw the triage nurse and 532 patients saw the GP. The median age of the triaged patients was 50 years (1st quartile 40 years, 3rd quartile 54), for the GP it was 46 (22, 58).

Note how the quartiles show an uneven distribution around the median, so the data cannot be normally distributed and a non-parametric test is appropriate.

The statistician used a “Mann-Whitney U test” to test the hypothesis that there is no difference between the ages of the two groups.

This gave a Mann-Whitney U value of 133 Wilcoxon W 200 with a P value of < 0.001. Ignore the actual U value but concentrate on the P value, which in this case suggests that the triage nurse’s patients were very highly significantly older than those who saw the GP.

Caution

The “Wilcoxon signed rank test”, “Kruskal Wallis” and “Friedman” tests are other non-parametric tests. Do not be put off by the names – go straight to the P value.

Mr. T. Dhasaratharaman

Statistician

Statistical Non Parametric Mann – Whitney U test

Statistical Non Parametric Mann – Whitney U test

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