I'm using 50,000 values of data. I can't perform a K.Wallis test in R because my data amount is bigger than 5000. Therefore I considered to use Kolmogorov-Smirnov test.
I guess that my data follows a normal distribution. However, I would like to be sure using the Ks.test. In R script I wrote:
ks.test(dat$Si,"pnorm",alternative = c("two.sided"),exact = NULL)
R results are:
data: dat$Si D = 0.904, p-value < 2.2e-16 alternative hypothesis: two-sided Mensajes de aviso perdidos In ks.test(dat$Si, "pnorm", alternative = c("two.sided"), exact = NULL) : ties should not be present for the Kolmogorov-Smirnov test
What does it mean? My result follows a normal distribution or not? I guess that If I have this small p-value, it means that I must reject the Null Hypothesis. So they don't follow a normal distribution.
Am I doing something wrong?