As a scientist, it depends on what you mean by wrong. It's nice to push an array of numbers through some array operation. If some of the outputs look like NaN o

As a scientist, it depends on what you mean by wrong. It's nice to push an array of numbers through some array operation. If some of the outputs look like NaN or Inf then that tells me something went wrong in the operation and to take a closer look. If some optimizer was told that NaN or Inf couldn't happen, meaning that they wouldn't be generated, then some of this output could be pure nonsense and I wouldn't notice. As NaNs propagate through the calculation they're extremely helpful to indicate something went wrong somewhere in the call chain. NaN is also very useful itself as a data missing value in an array. If you have some regularly sampled data, with some items missing, it makes a lot of things easier to populate the holes with values which guarantee they won't produce misleading output.