A nuisance parameter is like a pesky bug that wants to hang out with you but you don't really want it there. Imagine you have a toy car that you want to race with your friend, but there's a little bug crawling around on the track. That little bug is a nuisance, because it's not really part of the race and it might get in the way of the cars.
In science and statistics, there are some things called nuisance parameters that are kind of like that bug. When we do experiments or collect data, we usually have some specific things we want to measure or test, and everything else is just kind of there in the background. Maybe we're interested in how exercise affects weight loss, for example, but we also know that age or gender might have some influence too. Those extra factors are nuisance parameters.
Just like the bug on the race track, nuisance parameters can be annoying or get in the way, but they're not really the main focus of the experiment. We don't want to ignore them completely, because they could still have an impact on the results, but we also don't want to spend too much time or energy dealing with them if we don't have to. Sometimes we can find clever ways to account for nuisance parameters so that they don't cause too much trouble, and sometimes we just have to accept that they're there and try to work around them as best we can.