Definition of Skewness:
Skewness refers to distortion or asymmetry in a symmetrical bell curve, or normal distribution, in a set of data. If the curve is shifted to the left or to the right, it is said to be skewed. Skewness can be quantified as a representation of the extent to which a given distribution varies from a normal distribution. A normal distribution has a skew of zero, while a lognormal distribution, for example, would exhibit some degree of right-skew.
The three probability distributions depicted below are positively-skewed (or right-skewed) to an increasing degree. Negatively-skewed distributions are also known as left-skewed distributions. Skewness is used along with kurtosis to better judge the likelihood of events falling in the tails of a probability distribution.
Degree to which a statistical distribution is not in balance around the mean (is asymmetrical or lopsided), a perfectly symmetrical distribution having a value of 0. Distributions with extreme values (outliners) above the mean have positive skew, and the distributions with outliners below the mean have negative skew.
How to use Skewness in a sentence?
- Distributions can exhibit right (positive) skewness or left (negative) skewness to varying degrees.
- Sometimes you will not be able to figure out why things are happening and the skewness will weigh on your mind.
- There was some skewness in the data representation which was concerning the statistician because if the data is not consistent then that is a problem.
- The skewness of the graph rendered it unbelievable because the stats were off in certain areas and I knew it.
- Investors note skewness when judging a return distribution because it, like kurtosis, considers the extremes of the data set rather than focusing solely on the average.
- Skewness, in statistics, is the degree of distortion from the symmetrical bell curve in a probability distribution.
Meaning of Skewness & Skewness Definition