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I just completed teaching a 12 week business stats course!  Whew.  We covered normal distribution curves in the class.  Normal distribution curves are vitally important when analyzing a portion of the whole or population; a portion of the whole is called a sample.  Analyzing a sample is less expensive and takes less time; however, one must make sure he or she has a good sample to infer things about or make decisions regarding the whole or population.  Companies such as Coca-Cola rely on this type of data analysis; just think of those taste tests you have participated in (you are one of many samples).  After going through this course, I now realize what is meant by “grading on a curve” and I am no spring chicken (this means at least two things: 1) one is never too old to learn something and 2) one is never too old to have ‘aha’ moments.  I was often the beneficiary of a teacher’s kindness when he or she graded on a curve.  “Grading on a curve” simply means that the teacher leveled the playing field.  For example, those who scored low on a test; no worries because their score was given a little boost.  Those who scored high on the test; no worries because they still had a high score.  I did not grade on a curve in this class.  And on the last day of class, a student commended me for not grading on a curve.  Again, one pro of grading on a curve is that all scores are normalized or ‘equalized’ so that every one is playing on the same field.  However, one con of grading on a curve as this student and I discussed is this: we give students a false sense of confidence that they are doing okay; when actually they are not.  If these students who have benefited from teachers that grade on a curve go to college, what if college teachers don’t grade on a curve?  College bound students are in store for a rude reality check!  I wonder if those countries that typically and historically out score the United States in math grade on a curve?


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