Hi,

I have data measurements (eg, running cost) that keep coming in to the system continuously. There are two groups (say X and Y) that I want to compare. I want to see which one is systematically larger (H1: X > Y?). However, I have a couple of issue with my data: i) I can't assume normality of data; and ii) my data keeps getting deleted periodically. And when data is deleted it's no longer available to me. So at any given moment, X and Y may have lots of data, or only a few measurements.

Based on this, I was suggested to use Wilcoxon Rank Sum Test since it doesn't assume normality. However, I couldn't find any information about:

When can I apply Wilcoxon? or what the minimum sample size be?

This is important since at any moment, X and Y may have only a few data points (say 2 datapoints each); hence, running Wilcoxon on such sample would be misleading.

Any thoughts on this?

I have data measurements (eg, running cost) that keep coming in to the system continuously. There are two groups (say X and Y) that I want to compare. I want to see which one is systematically larger (H1: X > Y?). However, I have a couple of issue with my data: i) I can't assume normality of data; and ii) my data keeps getting deleted periodically. And when data is deleted it's no longer available to me. So at any given moment, X and Y may have lots of data, or only a few measurements.

Based on this, I was suggested to use Wilcoxon Rank Sum Test since it doesn't assume normality. However, I couldn't find any information about:

When can I apply Wilcoxon? or what the minimum sample size be?

This is important since at any moment, X and Y may have only a few data points (say 2 datapoints each); hence, running Wilcoxon on such sample would be misleading.

Any thoughts on this?

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