Why would you use the Mann-Whitney U test instead of a two sample t 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. The Mann-Whitney U test is often considered the nonparametric alternative to the independent t-test although this is not always the case.

Does data have to be normally distributed for t-test?

A t-test is a statistic method used to determine if there is a significant difference between the means of two groups based on a sample of data. Among these assumptions, the data must be randomly sampled from the population of interest and the data variables must follow a normal distribution.

Is Mann-Whitney test same as Mann-Whitney U test?

The Mann Whitney U test, sometimes called the Mann Whitney Wilcoxon Test or the Wilcoxon Rank Sum Test, is used to test whether two samples are likely to derive from the same population (i.e., that the two populations have the same shape).

Which is better the t test or the Mann Whitney test?

For larger sample size, the distribution is approximately normal For all sample sizes, the Mann Whitney test has more power than the t-test, and this by a factor of 2 to 3 times more power For all samples sizes, the Mann Whitney test has greater type I error, and this by a factor or 2 – 3

How does the two sample t test work?

The two-sample t-test allows us to test the null hypothesis that the population means of two groups are equal, based on samples from each of the two groups. In its simplest form, it assumes that in the population, the variable/quantity of interest X follows a normal distribution in the first group and is in the second group.

Is the t-test valid when x does not follow a normal distribution?

In fact, as the sample size in the two groups gets large, the t-test is valid (i.e. the type 1 error rate is controlled at 5%) even when X doesn’t follow a normal distribution. I think the most direct route to seeing why this is so, is to recall that the t-test is based on the two groups means and .

Can a large sample affect the Wilcoxon Mann Whitney test?

Zimmerman (2003) warns that the large-sample Wilcoxon-Mann-Whitney test can be strongly influenced by unequal variances of treatment groups even when sample sizes are equal. Hart (2001) notes that the Wilcoxon-Mann-Whitney test is a test of both location and shape – not as most researchers consider it a test of difference between medians.

Why would you use the Mann-Whitney U test instead of a two sample t-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. The Mann-Whitney U test is often considered the nonparametric alternative to the independent t-test although this is not always the case.

What is one difference between the Mann Whitney U and the median test?

The Mann-Whitney test is a test of both location and shape. Given two independent samples, it tests whether one variable tends to have values higher than the other. One distribution is shifted 0.75 units to the right: the medians differ by 0.75 units but the shapes are identical.

What is the difference between at test and a chi-square?

A t-test tests a null hypothesis about two means; most often, it tests the hypothesis that two means are equal, or that the difference between them is zero. A chi-square test tests a null hypothesis about the relationship between two variables.

Does Mann-Whitney use mean or median?

The standard deviation of the two groups is obviously very different. But since the Mann-Whitney test analyzes only the ranks, it does not see a substantial difference between the groups. The Mann-Whitney test compares the mean ranks — it does not compare medians and does not compare distributions.

How do you explain Mann-Whitney U test?

Mann-Whitney U test is the non-parametric alternative test to the independent sample t-test. It is a non-parametric test that is used to compare two sample means that come from the same population, and used to test whether two sample means are equal or not.

Which is better chi square or Mann Whitney?

Based on responses it seems there is no clear answer and that either approach (chi square or mann-whitney) could be used. I found no clear answer in searchers online, indeed people were making cases for both tests. Jochen – I like you frank approach. I think the work is explorative and therefore one can test the data in different ways.

When to use a mw or chi square test?

MW is for continuous data. Given the kind of data you have you will face a lot of “ties” rendering the results of this test questionable. Thus I would prefer to test the hypothesis that the frequency distributions (over the categories “time”) in the two groups are equal (-> Chi² test).

Which is better Fisher’s exact or chi square?

But your point is well-taken, and interesting food for thought (to be continued)… While Fisher’s exact test is superior to chi-square, ODA is superior to Fisher’s exact test except in purely binary problems, in which case the two methods converge.