I have been stuck with a couple of problems, so I decided to open an account here to post these questions of mine. Sorry if I am posting this in the wrong section.
I want to analyze a frequency distribution for neuronal currents before and after a treatment. I have two types of neurons; the values for each are depicted in the graph as red and black. I measure the interevent interval (IEI), that is, the time at which events (currents) occur (which is related to frequency; the shorter the interevent interval the higher the frequency). The most common test for frequency distribution people use in this field are Kolmogorov–Smirnov tests (ks-test).
Now my problem: The ks test is so sensitive that I always get a significant result. I can “see” from the graph that in the control condition the frequency distributions are very similar. On the “treatment” condition on the other hand differences in frequency distribution between both neurons are considerably bigger. But even when this is the case I always get a highly significant result for both cases
If I compare the distributions for red vs black neuron for control in GraphPad prism I get:
Kolmogorov-Smirnov test/ P value < 0,0001/ Exact or approximate P value? Approximate/ P value summary ****/ Significantly different? (P < 0.01) Yes/ Kolmogorov-Smirnov D 0,1244
When I compare red vs black neuron for “treatment” I get
P value < 0,0001/ Exact or approximate P value? Approximate/ P value summary ****/ Significantly different? (P < 0.01) Yes/ Kolmogorov-Smirnov D 0,1861/
If I do the same analysis in R I get:
For “control”: D = 0.12418, p-value = 4.34e-08/ alternative hypothesis: two-sided
For “treatment” D = 0.1779, p-value = 1.71e-13/ alternative hypothesis: two-sided
I guess I can decide that something is significant in this case if p < 1e-09 o p < 1e-10, but I have never seen something like that expressed on a paper.
So; is there any other way to analyze such frequency distribution to better represent what the graph is showing? I was thinking on splitting the IEI in bins (for instance 0-100 msec, 100-200msec, etc) and compare means, but then I would be losing information, as the most represented frequencies would “push” the data to that center. Also the error bars would be quite big, if I take the means for each individual.