I'm totally not a pro in statistics so i hope the question is not too simple!
I have two groups of patients. One group are smokers, the second are non-smokers.
I want to compare the level of some hormones in the two groups to see if they are influenced by smoking. On many articles I found researchers using Mann-Whitney test to see if the means are significantly different among the two groups. But reading around t-test i'm not sure anymore about which test should I use... unpaired t-test? unpaired t-test with Welch correlation? Mann-Whitney? one-tailed, two-tailed?
Can someone explain me which test to use is this situation and why? (in a way comprehensible by normal people?)
Some more info: The two groups are N = 102 (smokers) and 194 (non-smokers), and i red that for N > 30 unpaired t-test and mann-whitney should be similar but applying three tests i got three different p values:
- Unpaired t-test: p = 0.0245
- Unpaired t-test with Welch correction: p = 0.0318
- Mann-Whithney: p = 0.0620
As you can see p values varies a lot and with Mann-Whitney significance is even lost (>0.05)!
Update 2: Also correlation between number of daily cigarettes and hormone level gives strange results: Spearman test gives r = 0.1786 and p = 0.0725, while Pearson gives r = 0.2472 and p = 0.0123. So again with a parametric test the result is significative while with a non-parametric is not!!! I suppose that in this case non-parametric is more correct since we cannot be sure to have the right number of cigarettes, especially since this is a retrospective study.