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It is often necessary to compare two groups while doing data analysis or carrying out research. But there are times when the traditional t-test assumptions are not fully satisfied with the data, so what do we do? That is when Mann-Whitney U Test becomes helpful. Despite being a nonparametric statistical test, it can be very useful for these sorts of analyses. Let us see why is that important for you.
The Mann-Whitney U Test is appropriate whenever there’s potential for two independent groups to differ in rank such as in economics and social science research. Rather than using the t-test which presupposes normal distribution, this test is non-parametric meaning it can be used for otherwise skewed datasets. This test works by conducting a ranking of all observations for the two given groups and instead of tests based on raw data.
For instance, Mann-Whitney U Test is the most appropriate one when one wishes to compare the effect of two medicines without taking the effectiveness measurements on a uniform scale but on a scaled or ranked basis.
Join the data of both groups and order them before ranking them. When assigning ranks to the values, adjust for ties as deemed necessary. Determine the total ranks for each of the groups. From the total ranks, determine the U statistic. Use the critical statistical value for the U statistic from tables in order to establish a one-tailed relationship between two variables or use the p-value to investigate whether the U statistic is small.
It would be prudent to pay close attention to the Mann-Whitney U Test as it can be useful in analyzing data in accurate ways pertinent to biology, medicine, psychology, etc. It permits you to handle real-world data which often is not normally distributed and thus has minimum stringent assumptions. Therefore, researchers will be glad to know that valid conclusions can still be made in unfavorable situations.
Studies in health care, that is measuring the patient satisfaction score in treating two different groups. Pharmaceutical industry drug evaluation which relies on pain as an ordinal measure in determining whether a drug works. An aspect of behavioral science is measuring differences in the response patterns between two groups of consumers. Interpreting the Results If the relevant value attains a significant result (common criteria is p < 0.05) it follows that there is a difference in the central measures of the two groups.
The direction and the absolute measure of the difference is however not provided, in this case, by the Mann-Whitney U Test. By virtue of having to restate the empirical relationships constellations some post-hoc analysis or tests may be required. Limitations to Consider Though powerful and versatile the Mann-Whitney U Test has its limitations Exporting means is omitted in the test and only medians are compared.
The strength of this test is also affected by highly unequal group sizes. It is expected that the shape of the distributions is similar for the two groups (but not necessarily the same).
The Mann-Whitney U Test is ideal for those researchers who handle data that has non-parametric or ordinal characteristics. Its capacity to be incorporated into the realities of the world is what makes it indispensable. It’s essential when embarking in such activities as result analysis from an experimental study, conducting data from survey analyses or data from clinical trials as it may direct you in obtaining significant data without the requirement of a strict parameters.
After reading this article, I trust that you will be in a position to properly apply the Mann-Whitney U Test to your research and thereby ensure that your analysis results are of good quality and trustworthy in relation to any prevailing conditions that the data may have.
