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Shape of the curve when the majority of the scores fall at one end of the distribution, with the remainder of the scores tapering off as reflected in the long tail of the distribution
This is a reference to the shape of a histogram of tree lengths that can be produced after searching through treespace. Studies of random datasets have shown that the distribution of tree lengths is approximately normal, whereas in general datasets with a reasonable amount of signal have few shortest trees and few trees nearly as short. There is a G-statistic for the skewness of a histrogram.
Skewness characterises the degree of asymmetry of a distribution around its mean. Positive skewness indicates a distribution with an asymmetric tail extending towards more positive values. Negative skewness indicates a distribution with an asymmetric tail extending towards more negative values.
Negative skewness means there is a substantial probability of a big negative return. Positive skewness means that there is a greater-than-normal probability of a big positive return.
an oblique or slanting asymmetry
indicates any asymmetric "leaning" to either left or right of a probability distribution. Skewness is the third 'moment' of a distribution.
Skewness provides an indication of the how asymmetric the distribution is for a given sample. When estimated using the third moment, a value of 0 indicates a normal asymmetric distribution. A positive value indicates a positive skew (the right tail is longer than the left). A negative value indicates a negative skew (the left tail is longer than the right). Skewness values greater than 1 or less than -1 indicate a non-normal distribution.
Skewness is the degree of asymmetry in a frequency distribution. In positively skewed distributions the scores are piled up at the lower end of the distribution, to the left of the mode. In a negatively skewed distribution the scores are piled up at the high end of the distribution, to the right of the mode.
Skewness is a measure of lack of symmetry of a distribution.
See 'Non-normal distributions'.
Statistical term describing the third central moment about the mean, a measure of asymmetry.
Skewness measures the deviation of the distribution from symmetry.
Based upon the third moment around the mean, Skewness represents the level of symmetry or lack there of within the distribution of a funds rates of return around the average rate of return. Fund performance with a skewness greater than 0 is said to be positively skewed and indicates that returns larger than the average rate of return are more widely distributed. Fund performance with a skewness equal to 0 is symmetrical. An example of this are normally distributed rates of return. Fund performance with a skewness less than 0 is said to be negatively skewed and indicates that returns less than the average rate of return are more widely distributed.
A statistic which measures the lack of symmetry in a distribution. A plot of a skewed distribution would show a long tail to either the left or the right. Distributions with a longer upper tail are said to be positively (right) skewed, while those with a longer lower tail are negatively (left) skewed. The skewness of data is usually measured through a coefficient of skewness which is zero for symmetric distributions such as the normal or uniform distribution, is greater than zero for positively skewed data, and is less than zero for negatively skewed distributions. To judge whether data departs significantly from a normal distribution, a standardized skewness statistic can also be computed. Skewness is calculated in the One Variable Analysis Statlet.
Characterizes the degree of asymmetry of the distribution around its mean. Positive skews indicate asymmetric tail extending toward positive values (right-hand side). Negative skewness implies asymmetry toward negative values (left-hand side).