Skewness
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Please note that the content of this book primarily consists of articles available from Wikipedia or other free sources online. In probability theory and statistics, skewness is a measure of the asymmetry of the probability distribution of a real-valued random variable. "Many textbooks," a recent article points out, "teach a rule of thumb stating that the mean is right of the median under right skew, and left of the median under left skew. This rule fails with surprising frequency. It can fail in multimodal distributions, or in distributions where one tail is long but the other is heavy. Most ...
Please note that the content of this book primarily consists of articles available from Wikipedia or other free sources online. In probability theory and statistics, skewness is a measure of the asymmetry of the probability distribution of a real-valued random variable. "Many textbooks," a recent article points out, "teach a rule of thumb stating that the mean is right of the median under right skew, and left of the median under left skew. This rule fails with surprising frequency. It can fail in multimodal distributions, or in distributions where one tail is long but the other is heavy. Most commonly, though, the rule fails in discrete distributions where the areas to the left and right of the median are not equal. Such distributions not only contradict the textbook relationship between mean, median, and skew, they also contradict the textbook interpretation of the median." Skewness has benefits in many areas. Many simplistic models assume normal distribution; i.e., data are symmetric about the mean. The normal distribution has a skewness of zero. But in reality, data points may not be perfectly symmetric. So, an understanding of the skewness of the dataset indicates whether deviations from the mean are going to be positive or negative.
