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This was explicitly not the case in this class. A test curved to 85 points was worth less than another test curved to 90 points.

And honestly, even if each test was normalized to 100 points after the curve, I would still object (on ethical grounds, not mathematical) to not being given >100% when the test is curved to below my score.

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Ah, I didn't catch that. That's... annoying.

Perhaps you might object mathematically too. I think of a curve as fitting the test scores to a specific distribution as a corrective to testing error. It's not the fitting that's the issue, it's how justified the target distribution is. If you truncate it, you have to justify doing that somehow (all prior test results had that distribution?). Of course, if the sample distribution squashes it enough, you might end up clustered at 100% anyway, but...

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