Why use mann-whitney U test rather than independent sample test?

[Charles007]

why use non parametric mann-whitney U test rather than parametric independent sample test.

I have a question regarding this because i just complete two question, as one I am using parametric tests, independent sample test, solve the mean selling price of a home with a conservatory different from the mean selling price of a home without conservatory.

I also used non parametric tests, mann-whitney U test , these two test obtain the same result.

can anyone tell me why it's is most appropriate in testing the hypothesis using non-parametric mann-whitney U test?

Thank you very much for your help. I will wait online.

 

[Nino MMP]

Nonparametric tests are useful when the assumptions of parametric tests, such as normality, are not met. The Mann-Whitney U test is a nonparametric alternative to the two-sample t-test and can be used when the data is not normally distributed or when the sample sizes are small. It is also more robust than the t-test when there are outliers present in the data. Additionally, the Mann-Whitney U test does not make any assumptions about the population means or variances, which makes it a better choice for testing the hypothesis when dealing with unknown population characteristics.

 

[Blueshift5]

There are several reasons why the Mann-Whitney U test may be more appropriate in testing a hypothesis than the independent sample t-test.

Firstly, the Mann-Whitney U test does not assume that the data follows a specific distribution, whereas the independent sample t-test assumes that the data is normally distributed. This means that the Mann-Whitney U test can be used for data that is not normally distributed, making it a more versatile test.

Secondly, the Mann-Whitney U test is more robust to outliers and extreme values in the data. This means that the results of the test will not be heavily influenced by these values, making it a more reliable test in situations where outliers may be present.

Additionally, the Mann-Whitney U test does not require the assumption of equal variances between the two groups, whereas the independent sample t-test does. This means that the Mann-Whitney U test can be used when the variances of the two groups are not equal, making it a more flexible test.

Finally, the Mann-Whitney U test is non-parametric, meaning that it does not make any assumptions about the underlying population parameters. This makes it a more robust test in situations where the assumptions of parametric tests may not be met.

Overall, the Mann-Whitney U test is a more appropriate choice when the data does not meet the assumptions of the independent sample t-test or when the data is not normally distributed. It is a reliable and versatile test that can provide accurate results in a variety of scenarios.