The reason is that the uniform distribution is very rare. Nearly no real world scenario were something is equally likely to be the values 2, 0 and -2, and where

The reason is that the uniform distribution is very rare. Nearly no real world scenario were something is equally likely to be the values 2, 0 and -2, and where it's literally impossible to be -2.01. It exists but it's not the normal case. In noisy sensors case there's some arbitrary low probability of them being actually super wrong, if you go by true 10^-10 outlier bounds they will be useless for any practical use, while the 99% confidence range is a relatively small rent. More often you want some other distribution and say (-2, 2) and those are the 90th percentile interval not the absolute bounds, 0 is more likely than -2 and -3 is possible but rare. It's not bounds, you can ask you model for your 99th or 99.9th percentile value or whatever tolerance you want and get something outside of (-2,2).