I have been looking for nonparametric solutions to compare the means of the two groups but from what I have found Wilcoxon and Kruskal- Wallis tests assume that the data are independent.

Is there a test that suits my goal?

- Thread starter Patricia Nunes
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I have been looking for nonparametric solutions to compare the means of the two groups but from what I have found Wilcoxon and Kruskal- Wallis tests assume that the data are independent.

Is there a test that suits my goal?

Data do not have to be normally distributed for statistical analyses.

Regarding the rest of your contribution, I'm afraid that I do not understand your description.

With kind regards

Karabiner

Regarding the rest of your contribution, I'm afraid that I do not understand your description.

With kind regards

Karabiner

Basically, I need to compare two groups with a non-parametric test that has no data independence assumptions and I don't know what test can suit my goal.

So you have n (how many?) controls, and there are different kinds of impact (how many different kinds are there?

and/or does the kind of impact matter here at all?). And there are cases (how many?) with just 1 impact, and there

are cases (how many?) with two impacts?

With kind regards

Karabiner

and/or does the kind of impact matter here at all?). And there are cases (how many?) with just 1 impact, and there

are cases (how many?) with two impacts?

With kind regards

Karabiner

I have a dataset with paired numerical variables [control\ impacted] and a categorical variable "impact" that includes 17 different impacts. Each impact category has at least 8 observations but some have 30. I want to know if the impact modified the natural environment. Each impact is different from the other, which means that I can't combine the impacts with a few cases to increase the dataset and I need the effect of each one of the impacts.

Example:

| Control | Impacted | Impact |

| 2 | 10 | mining |

| 2 | 5 | mining |

| 3 | 50 | sewage |

| 3 | 40 | urbanization |

| 5 | 2 | impoundment |

In the example, sewage and urbanization came from the same study and the natural system is the control for the two impacts. Another situation of dependence is in "impoundment"in which the control is before impoundment and impacted is measured at the same place, but after impoundment.

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So, I am not sure what you mean if you say that you want to compare "means of two groups". There is a list of values, and some

of these observations belong to the same environment/study.

And it is not clear how the two impacts in the concerning studies are related - did they take place at the same time? As a sequence?

Overlapping? Does the effect of sewage in case #3 affect how much effect urbanization could have, or vice versa?

With kind regards

Karabiner

So, I am not sure what you mean if you say that you want to compare "means of two groups". There is a list of values, and some

of these observations belong to the same environment/study.

And it is not clear how the two impacts in the concerning studies are related - did they take place at the same time? As a sequence?

Overlapping? Does the effect of sewage in case #3 affect how much effect urbanization could have, or vice versa?

With kind regards

Karabiner

I have two kinds of situations in the same dataset: In some observations, the "control" corresponds to the pre-impact value, and in others, it corresponds to a natural comparable example.

I say I want to compare the means because some tests use the means of all observations in the variables to compare them, but actually, this is just a way to try to explain what I need. A test that compares the groups and tells me if they are equal or different would attend me.

The impacts are not necessarily related, one does not interfere with the other, and the organization in the same column is just for categorization.

Also, it is the differences that need to be well behaved, rather than the before/after data itself. What do these differences look like?

Or another option, design a randomization test that preserves the connection between the common control group.

Or another option, design a randomization test that preserves the connection between the common control group.