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Briefly: if you expect 10 failures in 10 years evenly distributed then you would expect 1 per year.

If it is a Possion distribution then you'd expect them to bunch up and get years with none, then them concentrated over a few years.

Possion distributions usually imply a time factor. That's concerning in this case as it implies maintenance isn't effective.




how would this apply to disparate aircraft on different maintenance cycles?


It has nothing to do with aircraft, and everything to do with random chance. Basically, it is more likely for time-series data to have clumps than to be perfectly even.

Imagine you throw a three darts a board. Which is more likely: all darts are equidistant, or 2 of the darts are closer to each other than the 3rd? Now imagine it's 3 events in time rather than 3 darts on a board. And the same logic applies to an arbitrary number of events, of course.


it seems to me that there is a lot of room between "perfectly even" and "twice on the same day"




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