Bootstrapping to compare two groups

Question

In the following code I use bootstrapping to calculate the C.I. and the p-value under the null hypothesis that two different fertilizers applied to tomato plants have no eff

Answer 1

EDIT2 : Hack deleted as it was a wrong solution. Instead one has to use the argument strata of the boot function :

total <- c(x,y)
id <- as.factor(c(rep("x",length(x)),rep("y",length(y))))
b <- boot(total, diff, strata=id, R = 10000)
...

Be aware you're not going to get even close to a correct estimate of your p.value :

x <- c(1.4,2.3,2.9,1.5,1.1)
y <- c(23.7,26.6,28.5,14.2,17.9,24.3)

total <- c(x,y)

b <- boot(total, diff, strata=id, R = 10000)
ci <- boot.ci(b)
p.value <- sum(b$t>=b$t0)/b$R
> p.value
[1] 0.5162

How would you explain a p-value of 0.51 for two samples where all values of the second are higher than the highest value of the first?

The above code is fine to get a -biased- estimate of the confidence interval, but the significance testing about the difference should be done by permutation over the complete dataset.