Skewness is a measure of the lack of symmetry, or the lopsidedness, of a distribution. In other words, one “tail” of the distribution is longer than another. For example, in Figure 8.9, Distribution A’s right tail is longer than its left tail, corresponding to a smaller number of occurrences at the high end of the distribution. This is a positively skewed distribution. Because the tail on the right, where higher values are, is longer, we call it positively skewed. This might be the case when you have a test that is very difficult, such that only a few people get scores that are relatively high
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