P value calculation I have three groups of experiments. For each experiment I am looking for the percentage of occurrence of case x.
In the first group I have 15 experiments. The case x was seen 10.191% of the total time for 15 experiments.
In the second group I have 6 experiments. The percentage of x is 1.564%.
In the third group I have 3 experiments. The percentage of x is 0%.
I want to show that occurrence of case x  significantly decreased from group one to two and to three. Thus I want to calculate the p-values.  Can anyone tell me how to do it?
Edit:
The number of measurements for the first group is 22568 and 10.191% of these measurements are case x.
The second group has 1854 measurements (1.564% are x) and the third group has 1164 measurements (0% x)
 A: Since numbers are available (obtained from percentages), chi-square gives following result (in R code): 
> M
      [,1] [,2]
[1,] 22568 2300
[2,]  1854   29
[3,]  1164    0
> 
> chisq.test(M)

        Pearson's Chi-squared test

data:  M
X-squared = 246.59, df = 2, p-value < 0.00000000000000022

Edit: I should probably take no_x and x counts rather than total_N and x counts: 
> M
      [,1] [,2]
[1,] 20268 2300
[2,]  1825   29
[3,]  1164    0
> 
> chisq.test(M)

        Pearson's Chi-squared test

data:  M
X-squared = 276.24, df = 2, p-value < 0.00000000000000022

A: You can use a Wald approximation for confidence intervals for the first set of experiments, and probably the second.  For the third, you can't calculate confidence intervals at all, but you can test whether any of the other experiments were significantly different from zero.
Wikipedia has a formula for the Wald and other approximations and this paper: Interval Estimation for a Binomial Proportion, describes the various approximations in more detail.
You should probably test for differences between the samples in each experiment group (which is what I assume Aniko is implying.  If there are big differences between the 15 experiments in the first group for instance, it would call into question these simple confidence intervals, and you might want to consider an effects model of some sort.
