[BioC] Mann Whitney

[Caroline Lemerle]

Hi Patricia,

i believe the result is the same for your two examples because the
ranks are the same (this test only sees how the values rank)
for 2 sets of size 3, you can never get a p-value lower than the one
you get for your two examples simply because that configuration (all
values in one are higher than those in the other) is the least likely
to occur. you might want to try other tests

Caroline

On Dec 11, 2007 6:21 PM, Patricia Garcia
<patricia.garcia.gonzalez at gmail.com> wrote:
> Hi
> I'm trying to use a Mann-Whitney test in a PCR data, like this:
>
>
> # Case the samples are similar:
> x1 <- c(19.0370805,17.51822,18.524912)
> y1 <- c(20.541484,22.039175,20.542968)
> w1 <- wilcox.test(x1,y1, paired = FALSE, alternative = c("two.sided"))
>
>
>         Wilcoxon rank sum test
>
>     data:  m and f
>     W = 0, p-value = 0.1
>     alternative hypothesis: true location shift is not equal to 0
>
> # Case the samples are not similar:
> x2 <- c(3.9934205,    3.499646,    4.489782)
> y2 <- c(20.541484,22.039175,20.542968)
> w2 <- wilcox.test(m,f, paired = FALSE, alternative = c("two.sided"))
>
>
>         Wilcoxon rank sum test
>
>     data:  m and f
>     W = 0, p-value = 0.1
>     alternative hypothesis: true location shift is not equal to 0
>
>
>
> I obtain the same result: pvalue > 0.05, so i don't reject the null
> hypothesis, that both samples came from the same distribution.
> If i test other numbers the result is the same always.
> Thanks.
>
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