I have two groups (healthy and disease, different sample sizes) of data, pooled from the individual subject's measurements of a certain region of the brain. The size of the brain region may be different from each individual and the sample sizes were different in these two groups. I have just carried out the ks.test using R on the data. They are not normally distributed.

As such, I ended up having 2500 samples for the healthy group and 1000 samples for the disease group.

I have plotted the histogram of these two groups (using normalized counts for y axis) and they appeared to be two different distributions, with overlapping. I would like to determine the cut-off value between two groups. What would be the correct way to go about it?

  • $\begingroup$ Follow the advice from @FrankHarrell in his answer. Much better than a cutoff is to describe and compare the full distributions for the two groups. $\endgroup$
    – EdM
    Jul 30, 2016 at 14:12

1 Answer 1


Forcing any continuous assessment to be classified using a cutoff/threshold is ultimately non-reproducible, arbitrary, and has very low precision. Plus if you use the data to select the cutoff you will need to bootstrap the whole process to get a reasonable measure of variability/stability.

Natura non facit saltus (Nature does not make jumps) -- Gottfried Wilhelm Leibniz


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