Well the assumption here is that we've got something we're measuring with a steady mean and either inherently noisy variation or some measurement error on top.
Lots of real-world samples follow the normal distribution, and anything that does should look roughly like that sim.
So there's no real need to use random numbers, but it's a very quick way of me getting data that looks like real data and I know I've got the standard deviation & mean correct and that there should be no anomalies.
My sim can only show one side of the story though, it can't show how often real issues are picked up. For that, we'd probably want to look at real-world data and investigate each reported issue to see what proportion are important (and then possibly try and see how many were missed).
Lots of real-world samples follow the normal distribution, and anything that does should look roughly like that sim.
So there's no real need to use random numbers, but it's a very quick way of me getting data that looks like real data and I know I've got the standard deviation & mean correct and that there should be no anomalies.
My sim can only show one side of the story though, it can't show how often real issues are picked up. For that, we'd probably want to look at real-world data and investigate each reported issue to see what proportion are important (and then possibly try and see how many were missed).