I am using R dixon.test in order to perform Q test for detection of outliers.
The p-values for high Q values are consistent with the table in Rorabacher,
D.B. (1991) http://www.flworkshop.com/sscs/dixon_test.pdf, but it seems that
small Q-values get problematic p-values. For example:
dixon.test(c(0.324,0.5,1,1.324),two.sided=T)
Q = 0.324, p-value = 1
dixon.test(c(0.324,0.5,1,1.324),two.sided=F)
Q = 0.324, p-value = 0.5
The p value I expect by simulation is ~0.83:
norm=matrix(nrow=1000,ncol=4)
for (i in 1:1000){
norm[i,]<-rnorm(4)
q[i]<-dixon.test(norm[i,])$statistic
p[i]<-dixon.test(norm[i,])$p.value>}<br>
hist(q)
expected.p.val<- length(which(q>0.324))/1000
In particular I am concerened from the misleading result of
dixon.test(c(1,1,2,2),two.sided=T)
Q = 0, p-value < 2.2e-16
I would appreciate any help with this issue,
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