I can give a real example. At work we were testing pulse shaping amplifiers for Geiger Muller tubes. They take a pulse in, shape it to get a pulse with a height proportional to the charge collected, and output a histogram of the frequency of pulse heights, with each bin representing how many pulses have a given amount of charge.

Ideally, of all components are the same, there is no jitter, and if you feed in a test signal from a generator with exactly the same area per pulse, you should see a histogram where every count is in a single bin.

In real life, components have tolerances, and readouts have jitter, so the counts spread out and you might see, with the same input, one device with, say, 100 counts in bin 60, while a comparably performing device might have 33 each in bins 58, 59, and 60.

This can be hard to compare visually as a PDF, but if you compare CDF's, you see S-curves with rising edges that only differ slightly in slope and position, making the test more intuitive.

If one line is to the right of the other everywhere, then the distribution is bigger everywhere. (“First order stochastic dominance” if you want to sound fancy.) I agree that CDFs are hard to interpret, but that is partly due to unfamiliarity.