I'm using ks.test (mydata, dnorm) on my data. I know some of my different variable samples (mydata1, mydata2, etc) must be normally distributed but the p value is always < 2.0^-16 (the 2.0 can change but not the exponent).
I want to test mydata against a normal distribution. What could I be doing wrong? I tried instead using rnorm to create a normal distribution: y = rnorm (68,mean=mydata, sd=mydata), where N= the sample size from mydata. Then I ran the k-s: ks.test (mydata,y). Should this work? One issue I had was that some of my data has a minimum value of 0, but rnorm ran as I have it above will potentially create negative numbers. Also some of my variables will likely be better tested against non- normal distributions (uniform etc.), but if I figure I should learn how to even use ks.test first. I used to use SPSS but am really trying to jump into R instead, but I find the help to assume too heavy of statistical knowledge. I'm guessing I have a long road before I get this, so any bits of information that may help me get a bit further will be appreciated! Thanks, kbrownk ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.