Why shift the Mann Whitney distribution?

In summary, the Mann-Whitney test involves determining the "T" or "U" statistic, which is the difference between the observed sum of ranks and the maximum possible value of the sum of ranks. This statistic is used to determine whether U falls within a certain interval, and it is parameterized by the sample sizes. However, the reason for using this shifted statistic instead of just the sum of ranks is unclear and some may argue that it is unnecessary.
  • #1
fadecomic
10
0
I'm reading up on the Mann Whitney test, and I can't wrap my head around one thing. Most of the test makes perfect sense. If two samples come from populations with similar medians, then the sum of ranks of both of those populations should hover around some expected value. The "T" or "U" statistic, depending on what you're reading, is determined, and one determines whether or not U falls within a certain interval. Fine. U is defined as the difference of the observed sum of ranks and either the minimum or maximum possible value of the sum of ranks (doesn't matter). The U distribution is parameterized by the two sample sizes and nothing more, as is the maximum possible U. That means the max possible U is nothing but a shift of the distribution. Why bother? Is there some advantage to doing it that way? Why not tabulate the distribution based on the sum of ranks only (which is done--it's called the "Wilcoxan test")?

Thanks.
 
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  • #2
Incidentally, the expected value for the U statistic is [tex](n_an_b)/2[/tex], which is easy to prove. So why does the Mann-Whitney test require a statistic that is shifted to a central value of [tex](n_an_b)/2[/tex]?
 

1. Why is it important to shift the Mann Whitney distribution?

Shifting the Mann Whitney distribution can help to identify differences between two groups or populations. It allows for more accurate comparisons and can provide insight into potential relationships or patterns.

2. How is the Mann Whitney distribution shifted?

The Mann Whitney distribution is shifted by adding a constant value to all data points in one of the groups being compared. This shifts the entire distribution without changing the shape or relative positions of the data points within each group.

3. What factors can influence the decision to shift the Mann Whitney distribution?

The decision to shift the Mann Whitney distribution may be influenced by the data being compared, the desired level of significance, and the research question being addressed. Additionally, the distribution may need to be shifted if there are concerns about the data violating assumptions of the Mann Whitney test.

4. Are there any limitations to shifting the Mann Whitney distribution?

Yes, there are limitations to shifting the Mann Whitney distribution. Shifting the distribution can alter the results of the Mann Whitney test and may not be appropriate if the data does not support it. Additionally, shifting the distribution may not be effective if the data is highly skewed or has extreme outliers.

5. Can shifting the Mann Whitney distribution be used for any type of data?

Shifting the Mann Whitney distribution can be used for continuous or ordinal data, but it is not appropriate for categorical data. Additionally, the distribution should be shifted in a way that maintains the underlying order of the data, so it may not be appropriate for data with complex relationships or nonlinear patterns.

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