I am exploring the cumulative distribution function of two samples. I am comparing the distribution of observations in which a focal animal was disturbed with the distribution of observations in which a focal animal was not disturbed as a function of distance to the disturbance.
Using program mark I have computed and plotted the ecdf for each population vs distance to disturbance. I have also used the Kolmogorov-Smirnov Test to test if the distributions are different. They are. What I would like to know is if there is a way to determine at what distance from the disturbance these distributions converge and are no longer different.
In playing around with this data I have noted that if I truncate the both samples to only include observations where the disturbance is greater than 0.7 km from the focal animal and rerun the K-S test the D statistic is no longer significant. When plotted the two distributions (using the truncated data) seem to be parallel and similar in shape. I interpret this as an indicator that the divergence in the distributions is occurring in the section of the data when the disturbance is closer than 0.7 km from the focal animal.
I understand that by truncating the observations I am reducing my sample size and effecting the calculation of the p-value. Is this line of thought statistically flawed to the point I am getting spurious results? Any hints on how to explore which section of the data is contributing the the divergent distributions?