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

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

What are the assumptions in the Mann Whitney U test?

The following assumptions must be met in order to run a Mann-Whitney U test: Treatment groups are independent of one another. Experimental units only receive one treatment and they do not overlap. The response variable of interest is ordinal or continuous.

Can you do multiple comparisons in Spearman Rho?

If you want to know differences between pairs, you could do Mann-Whitney U tests for each pair or test for correlations using a spearman rho for instance. The correction for multiple comparisons should be applied on results for those tests especially if you haven’t predefined hypotheses.

How to do multiple comparisons in nonparametric tests?

A simple approach could be: If you want to know differences between pairs, you could do Mann-Whitney U tests for each pair or test for correlations using a spearman rho for instance. The correction for multiple comparisons should be applied on results for those tests especially if you haven’t predefined hypotheses.

How does the Wilcoxon Mann Whitney ( WMW ) test work?

The Wilcoxon-Mann-Whitney (WMW) test consists of taking all the observations from the two groups and ranking them in order of size (ignoring group membership). The ranks of the observations from the first group (it doesn’t matter which group you choose) are then summed, and the test statistic is formed as

Which is the best alternative to the t test?

When this assumption is in doubt, the non-parametric Wilcoxon-Mann-Whitney (or rank sum ) test is sometimes suggested as an alternative to the t-test (e.g. the Wikipedia page on the t-test), which doesn’t rely on distributional assumptions. But is this necessarily a good ‘replacement’?

What are the assumptions in the Welch test?

The Welch test adds the assumption that the two groups comes from a normal distribution, but the variance in the two groups can be different. The Student t-test adds yet another assumption, that the variance should be equal in the two groups. But what do these assumptions imply?

Why is the U-test favoured over the t-test?

Despite its lower power, it is often favoured over the t-test because of the misconception that no assumptions have to be met for the test to be valid. In fact the basic assumptions of the two tests (namely that both samples are random samples and are mutually independent) are identical.

Is the t test robust to non normality?

As I described in a previous post, provided the sample size is moderately large, the two-sample t-test is robust to non-normality due to the central limit theorem.

When to use a permutation test instead of a t-test?

So the usual t-test (possibly allowing for unequal variances) can usually be used, provided the sample sizes are not too small and the distribution is not extremely skewed. But what if the sample size is small? One thought is to use a permutation test, based on computing the difference in sample means and permuting the group membership.