My data has following distribution:
I wish to determine if it is a Normal distribution, so:
> library(nortest) Warning message: package 'nortest' was built under R version 2.12.2 > sf.test(y) Error in sf.test(y) : sample size must be between 5 and 5000 > ad.test(y) Anderson-Darling normality test data: y A = 5487.108, p-value = Inf > cvm.test(y) Cramer-von Mises normality test data: y W = 855.7627, p-value = Inf > pearson.test(y) Pearson chi-square normality test data: y P = 2456556, p-value < 2.2e-16 > qqnorm(y); abline(0, 1)
When I do
qqnorm, I found
Can I conclude
y does not have a normal distribution, since the p-value = INF and the qqnorm did not fit the abline?
Should I expect all these p-values close to zero or one when my distribution is in normal distribution? How about the pearson.test?
How should I interpret the numbers (A, W and P) before the p-values?