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Define Skewness and Kurtosis
With respect to the measures of central variability and tendency, skewness and kurtosis are two comparable characteristics that allow understanding a distribution. It is possible for the two distributions to have the same standard deviation and mean but they might vary tremendously in their general appearance.
Skewness is usually a measure of the symmetry of a dataset. A dataset or a distribution appears the same to the right and left of the center point. Kurtosis is a factor that explains the shape of the probability distribution of a random variable. There are different interpretations of kurtosis and the ways of interpreting specific measures on the basis of those specific measures of kurtosis being used.
Skewness is the lack of symmetry or asymmetry in the frequency distribution shape. Estimates of skewness give the extent and direction of skewness. In a distribution that is symmetric, the median, mean, and mode are alike. The more the mean deviates from the mode, the higher is the skewness or symmetry. A skewed distribution is the one in which the median and the mean fall at diverse points in the distribution and the center of gravity is toward one or the other side.
Kurtosis measures the peakedness of the possible dissemination of an erratic variable that is legitimately esteemed. In a relative manner to skewness, kurtosis acts as a descriptor of the probable conveyance state. With reference to skewness precisely, there are different routes of quantifying the same for a notional dispersion and connecting routes of assessing it from the perspective of current inhabitants.
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