4 ms·

Sorry, by data, I meant the percentages add up to one (so that the integral equals 1), not the data it was derived from. Thanks for the correction. My method w

Sorry, by data, I meant the percentages add up to one (so that the integral equals 1), not the data it was derived from. Thanks for the correction. My method would be more fair, but is incorrect when applied to standard normalization. It should be percentiles. In a strict bell curve, if the top 20% get A's, then the bottom 20% would also have to fail (even if they didn't really get an F) so a pretty unfair system as you mentioned as grades are already predetermined :(

christopheraden 13y ago
Your first point is not just a property of the Gaussian, but of all distributions. It's the Second Axiom of Probability! Your method is incorrect as a method of normalization, but it seems we are in agreement that perhaps normalization itself is a silly method for this. Most instructors I've known that claimed they were "curving" were in fact using the percentile approach. Grading based on percentiles (top 10% get A's, next 20% get B's, 40% C's, 10% D, bottom 20% F, for example) allows you far more flexibility, as the percentiles don't even need to be symmetric. My grad courses usually only had three grades: A+, A, and "Please see me to discuss your future in the department".