I mean curves apply artificial scaling to data of course how one decides to apply them. In some contexts it means that there's always someone failing because in

I mean curves apply artificial scaling to data of course how one decides to apply them. In some contexts it means that there's always someone failing because in the pool of data you're fitting to, they performed the worst (relatively speaking) but perhaps only in that pool. Overall the worst performer could be excellent in a bigger/wider context. There's no standard "curving" procedure which leaves a lot of ambiguity and potential variation in the results. Often someone just means fitting it to a normal distribution and scaling accordingly. Not everything makes sense in a normal distribution even though people like to default to it in the absence of a known distribution. This gets even more complicated when you start talking about arbitrarily curving across different work, perhaps different courses, across different semesters and so on. Overall you may end up with some weird weighted distribution of your actual performance that's a bit nonsensical. I'm not here to say tests are great metrics but consistency in metrics is usually a good thing or at least a consistent process for attaining them.