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I have created a vector of length 400 with 6 unique values of relatively equal proportions (1-6). I have drawn two random samples from the vector, each $n=26$, 10,000 times, performed $t$-tests, added the $p$-values to a list and output a histogram of the $p$-value frequency. The histogram of the $p$-values shows a cyclical clumping effect for $p$-values greater than $0.7$. I would like to know why this occurs.

Here is my R-code:

set.seed(117)
cover1<-rep(1,100)
cover2<-rep(2,63)
cover3<-rep(3,59)
cover4<-rep(4,74)
cover5<-rep(5,56)
cover6<-rep(6,48)
landscape=c(cover1,cover2,cover3,cover4,cover5,cover6)

sample.size<-26
x<-10000
pval.list <-vector("list", length(x))
alpha <- 0.05
conf.level <- 1 - alpha
for(i in 1:x){

  s1 <- sample(landscape, size = sample.size, replace = TRUE, prob = NULL)
  s2 <- sample(landscape, size = sample.size, replace = TRUE, prob = NULL)
  result.t.test <- t.test(s1,s2, conf.level=conf.level)
  pval.list[i] <- result.t.test["p.value"] 
}
s<-pval.list
pvalue<-as.numeric(s)
hist(pvalue, main = paste("Histogram of" , "p-value"), breaks= 100)

Histogram of p-values

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The effect you're seeing is due to the small number of distinct values of your underlying population combined with your smallish sample size. In order to get a large value for your t-statistic (and therefore a high p-value) with a sample size of 26, you need to have a fairly extreme sample, but there aren't all that many ways to construct an extreme sample with this population, so you don't have all that many different large p-values coming out of the analysis - and consequently you have noticeably large gaps between them.

To confirm this, try running the same code with different values for sample.size:

enter image description here

enter image description here

As you can see, with a sample size of 100, the right tail of the graph has filled in a lot, and with a sample size of 1000, you're getting pretty close to a uniform distribution.

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