On 7/17/2009 12:13 PM, James Allsopp wrote:
Hi,
I'm trying to run Fisher's Exact test on the data below, but I'm not
sure how interpret the data shown. Can someone tell me what this is
saying? Looking at the numbers it should be that there's no significant
difference between the HDL and LDL, but a p-value of 1 seems high. Is
the low value in the LDL unbound making the test unstable and should I
be using an alternative?
The p value of 1 says that the data could not be any more consistent
with the hypothesis than they are. With the margins you have, there are
only 11 possible outcomes, and your table is the most probable one under
the hypothesis of independence.
You can see the probabilities of all possible outcomes using
dhyper(0:10, 11, 35, 10)
With rounding, I see
> round(dhyper(0:10, 11, 35, 10), 2)
[1] 0.05 0.19 0.32 0.27 0.13 0.04 0.01 0.00 0.00 0.00 0.00
where the results go from 0 to 10 in the lower right corner. You had a 2.
Duncan Murdoch
Best regards
James
data <-
matrix(c(27,8,9,2),nr=2,dimnames=list(c("HDL","LDL"),c("Bound","Unbound")))
data
Bound Unbound
HDL 27 9
LDL 8 2
fisher.test(data)
Fisher's Exact Test for Count Data
data: data
p-value = 1
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
0.06629276 4.88625959
sample estimates:
odds ratio
0.7545197
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