Ok, I work in a microbiology laboratory, and the tests I've run were in reference to the occurrence of some cellular proteins, and whether or not they are good indicators of certain things regarding cancer like whether or not the patient survived, length of survival, subsequent cancer, etc.

On to the data: All the tests that I ran were using non parametric tests. Do I use the Chi-square test to see if this is the case? Do I understand that if you get a successful result in the chi square test then your data are not parametric? In any case, I also ran non parametric tests because I've been told to look over a paper that is similar to this one (just testing different variables), and they ran non parametric tests like the Mann Whitney, Spearmans correlation, chi square, Kaplain Meier test etc.

If the chi-square does tell you whether your data is parametric, then most of my variables are (<.05). Am I to understand something would be non parametric if, let's say it had 3 possible values; 0, 1, and 2, indicating three levels of intensity, and it had 12 occurrences of 0, 3 occurrences of 1, and 1 occurrence of 2? In any case, if I am to compare values in where one is parametric and the other is non parametric...what is a more reliable test, spearmans rho or pearsons r?

Originally the investigator wanted me to use multivariate analysis, which I did, but if my data are nonparametric, then I am not able to do this, correct? The reason she wanted multivariate is because some of the dependent variables might exhibit some causation amongst themselves, such as subsequent cancer and length of life. I ended up using the Kruskal Wallis test instead.

I've run many different tests on the data, and a lot of them give very similar findings, which I assume is proper, but some of them do change the findings enough to achieve statistical significance in a certain direction...I want to make sure I can help her find what can and can not be said about the data.

Thank you!

Btw all tests were done using SPSS 17.