can I use z-score normalization even if it doesn't make sense for my data to be negative? I'm planning to use z-score as a Normalization Method for a Project but I noticed if I do that then I ll have a data in the range [-1, 1] which is wierd because I have data that doesn't make sense for it to have negative values. Let's say for example speed or distance, it doesn't make sense that speed would have a negative value after normalization! is it logical to think like this or am I wrong and it is perfectly fine to use z-score even if the data will be negative and it doesn't make sense?

ps: I know velocity can be negative if we are talking about Vectors but I meant to say that I have a discrete Values for Speed or Distance a.k.a Length of something which cannot be negative.
I think it doesn't really matter that there can be negative values after as long as you rescale your data correctly at the end.

Think about what positive/negative values mean for z-score. This has nothing to do with whether your use case (for example speed) can realistically have negative values or not. With z-score positive values simply mean that the value is above the group mean while negative values can be interpreted as the opposite.

As long as you are able to rescale after your model (to get your data back to the original interpretation) using z-score should be fine.