There was a post here about obtaining non-standard p-values for testing the correlation coefficient. The R-library SuppDists deals with this problem efficiently. library(SuppDists) plot(function(x)dPearson(x,N=23,rho=0.7),-1,1,ylim=c(0,10),ylab="density") plot(function(x)dPearson(x,N=23,rho=0),-1,1,add=TRUE,col="steelblue") plot(function(x)dPearson(x,N=23,rho=-.2),-1,1,add=TRUE,col="green") plot(function(x)dPearson(x,N=23,rho=.9),-1,1,add=TRUE,col="red");grid() legend("topleft", col=c("black","steelblue","red","green"),lty=1, legend=c("rho=0.7","rho=0","rho=-.2","rho=.9"))</pre> This is how it looks like, Now, let’s construct a table of critical values for some arbitrary or not significance levels. q=c(.025,.05,.075,.1,.15,.2) xtabs(qPearson(p=q, N=23, rho