I am trying to find the correlation between a dichotomous and a continuous variable.
From my ground work on this I found that I have to use independent t-test and the precondition for it is that the distribution of the variable has to be normal.
I performed Kolmogorov-Smirnov test for testing the normality and found that the continuous variable is non-normal and is skewed (for about 4,000 data points).
I did the Kolmogorov-Smirnov test for the entire range of variables. Should I split them into groups and do the test? I.e., say if I have
risk level (
0 = Not risky,
1 = Risky) and cholesterol levels, then should I:
Divide them into two groups, like
Risk level =0 (Cholestrol level) -> Apply KS Risk level =1 (Cholestrol level) -> Apply KS
Take them together and apply the test? (I performed it on the whole dataset only.)
After that, what test should I do if it is still non-normal?
EDIT: The above scenario was just a description I tried to provide for my problem. I have a dataset which contains more than 1000 variables and about 4000 samples. They are either continuous or categorical in nature. My task is to predict a dichotomous variable based on these variables (maybe come up with a logistic regression model). So I thought the initial investigation would involve finding the correlation between dichotomous and a continuous variable.
I was trying to see how the distribution of the variables are and hence tried to go to t-test. Here I found the normality as an issue. The Kolmogorov-Smirnov test gave a significance value of 0.00 in most of these variables.
Should I assume normality here? The skewness and kurtosis of these variables also show that the data is skewed (>0) in almost all cases.
As per the note given below I will investigate the point-biserial correlation further. But about the distribution of variables I am still unsure.