If you want to optimize for some outcome (renting cars at a known average price, with some known average penalty for promising cars you don’t have), you can just directly optimize for it.
But if you just want to get a picture of what’s going on, there aren’t going to be any non-arbitrary tests. Comparing the standard deviation to some scale of interest is just a useful piece of information people use to understand the problem. Feel free to set any arbitrary boundaries (or less arbitrary but still not optimal, e.g. “six sigma” business practices) you want.
The usual statistical test is comparing the standard deviation to your measurement precision.
In the car example above, you have a nice binomial distribution, which has a standard deviation of sqrt( N*p(1-p) ).
This is about sqrt(3), which is greater than your measurement precision, which gives you a good idea of what the noise looks like.
How exactly do I apply that test?
If you want to optimize for some outcome (renting cars at a known average price, with some known average penalty for promising cars you don’t have), you can just directly optimize for it.
But if you just want to get a picture of what’s going on, there aren’t going to be any non-arbitrary tests. Comparing the standard deviation to some scale of interest is just a useful piece of information people use to understand the problem. Feel free to set any arbitrary boundaries (or less arbitrary but still not optimal, e.g. “six sigma” business practices) you want.