I would like some input on a discussion I am currently having with a colleague regarding some measurements that were done recently.
I have some radiation physics data that is processed as a histogram. This histogram I believe can be modeled as a Poisson distribution since each value that goes into a given interval, or bin, is independent of any other. My colleague is arguing that this thinking is incorrect, but rather, bins that are closer together in the histogram are correlated, and as a result I am over-estimating the amount of error in my measurements.
Is this correct? To me, the formulation of a histogram is simply an intuitive data processing technique, wherein there is no "cross-talk" between bins.
There is obviously quite a bit of overlap here with regard to physics, so I will resort to analogy for the lay person: We are breaking-up heavy ions using shielding of varying types. We could refer to the heavy ion fragments as pieces of "mail", the detector as the "mailbox", a histogram bin as the "delivery day", and the total number of bins containing all the mail as the "month". Of course, integration over the histogram bins gives the total amount of mail delivered in a month. The peak delivery day in a given month is dependent on the amount of shielding between the beam and the detector. Following this analogy, my colleague is arguing that any two delivery days are correlated.