This mean
First, I am no expert but I am analyzing some marketing data.
I have information on two versions of the same site, and I have data
on the number of times people filled out a form on each version
of the site.
Sample data:
Site 1 Site 2
Filled out form 10 35
Did not fill out form 50 40
dat2 = matrix(c(10,50,35,40), ncol=2)
dat2
fisher.test(dat2)
> fisher.test(dat2)
Fisher's Exact Test for Count Data
data: dat2
p-value = 0.0002381
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
0.09056509 0.54780215
sample estimates:
odds ratio
0.2311144
I'm really not sure if I set up the test properly, but I can
obviously reject the null hypothesis given the low p-value.
Site 2 converts better than site 2 at a statistically significant
threshold.
Am I running my code wrong?
Can anyone help?
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On 11/17/2011 06:29 PM, Abraham Mathew wrote:> I have information on two versions of the same site, and I have data > on the number of times people filled out a form on each version > of the site. > > Sample data: > > Site 1 Site 2 > Filled out form 10 35 > > Did not fill out form 50 40 > > > dat2 = matrix(c(10,50,35,40), ncol=2) > dat2 > fisher.test(dat2) > >> fisher.test(dat2) > Fisher's Exact Test for Count Data > > data: dat2 > p-value = 0.0002381 > alternative hypothesis: true odds ratio is not equal to 1 > 95 percent confidence interval: > 0.09056509 0.54780215 > sample estimates: > odds ratio > 0.2311144 > > > I'm really not sure if I set up the test properly, but I can > obviously reject the null hypothesis given the low p-value. > Site 2 converts better than site 2 at a statistically significant > threshold. > > Am I running my code wrong?Your code is fine; your conclusion is valid (assuming you mean "...better than site 1..."). -- Patrick Breheny Assistant Professor Department of Biostatistics Department of Statistics University of Kentucky