👉 In statistics, a skewness is a measure of the asymmetry or non-normality of a distribution. It quantifies how much the data deviates from its mean. Skewness can be defined as an excess kurtosis value, which indicates how much the peak of the distribution is above or below the mean. Skewnesses are often used to evaluate the shape of a distribution and to identify outliers or other unusual features in a sample. They can also be used for statistical significance testing
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What is the definition of Skewness? 🙋
👉 Skewness is a measure of the asymmetry or kurtosis of a probability distribution. It describes how much data points are elongated in one direction compared to another, and can be used to assess whether a distribution is skewed left, right, or neither. In statistics, it's also known as "kurtosis" because it measures the degree of excess kurtosis (kurtosis = 3) of the probability density function (PDF). Skewness is related to the
What is the definition of Skewing? 🙋
👉 Skewness is a statistical measure that describes how much data values are spread out or concentrated in one direction. It is commonly used to assess whether a distribution of data is skewed, which means it has a longer tail on the right side compared to the left. Skewness can be positive (indicating an over-representation of extreme values) or negative (indicating an under-representation of extreme values).
