Skewness ant Types
Skewness, in statistics, is the degree of distortion from the symmetrical bell curve, or normal
distribution, in a set of data. Skewness can be negative, positive, zero or undefined. 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 depict increasing levels of right (or
positive) skewness. Distributions can also be left (negative) skewed. Skewness is used along
with kurtosis to better judge the likelihood of events falling in the tails of a probability
distribution.
Right Skewed
Key Takeaways
Skewness, in statistics, is the degree of distortion from the symmetrical bell curve in a
probability distribution.
Distributions can exhibit right (positive) skewness or left (negative) skewness to varying
degree.
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.
, Broadly speaking, there are two types of skewness: They are
(1) Positive skewness and
(2) Negative skewnes.
Positive skewness
A series is said to have positive skewness when the following characteristics are noticed:
Mean > Median > Mode.
The right tail of the curve is longer than its left tail, when the data are plotted through a
histogram, or a frequency polygon.
The formula of Skewness and its coefficient give positive figures.
Negative skewness
A series is said to have negative skewness when the following characteristics are noticed:
Mode> Median > Mode.
The left tail of the curve is longer than the right tail, when the data are plotted through a
histogram, or a frequency polygon.
The formula of skewness and its coefficient give negative figures.
Thus, a statistical distribution may be three types viz.
Symmetric
Positively skewed
Negatively skewed
Kurtosis
Kurtosis is a statistical measure that defines how heavily the tails of a distribution differ
from the tails of a normal distribution. In other words, kurtosis identifies whether the tails of
a given distribution contain extreme values.
Along with skewness, kurtosis is an important descriptive statistic of data distribution.
However, the two concepts must not be confused with each other. Skewness essentially
measures the symmetry of the distribution while kurtosis determines the heaviness of the
distribution tails.
In finance, kurtosis is used as a measure of financial risk. A large kurtosis is associated with a
high level of risk of an investment because it indicates that there are high probabilities of
extremely large and extremely small returns. On the other hand, a small kurtosis signals a
moderate level of risk because the probabilities of extreme returns are relatively low.
Skewness, in statistics, is the degree of distortion from the symmetrical bell curve, or normal
distribution, in a set of data. Skewness can be negative, positive, zero or undefined. 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 depict increasing levels of right (or
positive) skewness. Distributions can also be left (negative) skewed. Skewness is used along
with kurtosis to better judge the likelihood of events falling in the tails of a probability
distribution.
Right Skewed
Key Takeaways
Skewness, in statistics, is the degree of distortion from the symmetrical bell curve in a
probability distribution.
Distributions can exhibit right (positive) skewness or left (negative) skewness to varying
degree.
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.
, Broadly speaking, there are two types of skewness: They are
(1) Positive skewness and
(2) Negative skewnes.
Positive skewness
A series is said to have positive skewness when the following characteristics are noticed:
Mean > Median > Mode.
The right tail of the curve is longer than its left tail, when the data are plotted through a
histogram, or a frequency polygon.
The formula of Skewness and its coefficient give positive figures.
Negative skewness
A series is said to have negative skewness when the following characteristics are noticed:
Mode> Median > Mode.
The left tail of the curve is longer than the right tail, when the data are plotted through a
histogram, or a frequency polygon.
The formula of skewness and its coefficient give negative figures.
Thus, a statistical distribution may be three types viz.
Symmetric
Positively skewed
Negatively skewed
Kurtosis
Kurtosis is a statistical measure that defines how heavily the tails of a distribution differ
from the tails of a normal distribution. In other words, kurtosis identifies whether the tails of
a given distribution contain extreme values.
Along with skewness, kurtosis is an important descriptive statistic of data distribution.
However, the two concepts must not be confused with each other. Skewness essentially
measures the symmetry of the distribution while kurtosis determines the heaviness of the
distribution tails.
In finance, kurtosis is used as a measure of financial risk. A large kurtosis is associated with a
high level of risk of an investment because it indicates that there are high probabilities of
extremely large and extremely small returns. On the other hand, a small kurtosis signals a
moderate level of risk because the probabilities of extreme returns are relatively low.
