I am trying to check a hypothesis using a WIlcoxon rank sum test on R. I have my data in two separate arrays, X and Y. My initial hypothesis is that X's mean value is greater than Y's and obviously my alternative hypothesis would be that Y's is greater. I'm a bit confused as to how alternative works in this type of test: what is the "shift" that the test's results refer to and how can I understand that better through my problem described above?
wilcox.test(X, Y, conf.level=0.01, alternative="????")
Basically I'm looking to understand what would go in the ???? section.
EDIT: Just to clarify, I am working with a situation were I have to compare the damage a female game character does with that of a non-female character. I have saved the damage for women in X and that for other in Y. In either case I have 21 and 25 values for damage respectively.
I then run two tests: 1) to check whether the populations' distributions were normal (the women's seems to not be and the others' is iffy) and 2) check the variances for which the ratio came around 1.03. In the book I'm consulting about this it says that when distribution is not normal and I want to compare something common I need to conduct a Wilcoxon rank sum or Mann-Whitney test. An additional question would be whether I'm right to assume I can apply this test to my case, by having my initial assumption be "women's mean value of damage is higher than others'" and my alternative be "other's mean value of damage is higher than women's".
My initial hypothesis is that X's mean value is greater than Y's and obviously my alternative hypothesis would be that Y's is greater.This isn't quite right. a) The null hypothesis might be that X and Y have equal means, and then the alternative is that they do not. This is a two-sided hypothesis test. b) The null hypothesis might be that X has a greater mean than Y. In this case, the alternative hypothesis is that the mean of X is less than or equal to that of Y. This is a one-sided test. $\endgroup$
alternative =option. $\endgroup$
altein Wilcoxon tests. $\endgroup$