Measures Of Kurtosis In Statistics. Skewness tells you the amount and direction of skew departure from horizontal symmetry and kurtosis tells you how tall and sharp the central peak is. In statistics we use the kurtosis measure to describe the tailedness of the distribution as it describes the shape of it.
A uniform distribution would be the extreme case. In statistics a measure of kurtosis is a measure of the tailedness of the probability distribution of a real valued random variable. In statistics we use the kurtosis measure to describe the tailedness of the distribution as it describes the shape of it.
In statistics a measure of kurtosis is a measure of the tailedness of the probability distribution of a real valued random variable.
That is data sets with high kurtosis tend to have heavy tails or outliers. A distribution that has tails shaped in roughly the same way as any normal distribution not just the standard normal distribution is said to be mesokurtic. Whereas skewness differentiates extreme values in one versus the other tail kurtosis. The standard measure of kurtosis is based on a scaled version of the fourth moment of the data or population.
