IGNOU MCS 66 2026 2027 - Explain the concepts of skewness and kurtosis. A data scientist observ

Question

Explain the concepts of skewness and kurtosis. A data scientist observes two datasets with the same mean
and variance but different skewness and kurtosis. Explain how these measures help in understanding the
nature of the distributions. Also, explain the characteristics of a normally distributed dataset

03 Aug 2026

Answer :

Word Count : 559

Skewness and kurtosis are important statistical measures used to understand the shape and characteristics of a probability distribution. While mean and variance describe the central location and spread of data, skewness and kurtosis provide additional information about the symmetry and tail behaviour of a distribution. Therefore, two datasets can have the same mean and variance but still have substantially different distributional patterns. Skewness measures the degree and direction of asymmetry in a distribution around its mean. A perfectly symmetric distribution has zero skewness. Positive skewness indicates that the distribution has a longer or heavier tail on the right side, with a few relatively large observations pulling the mean upward. In such a distribution, the mean is generally greater than the median. Negative skewness indicates a longer or heavier tail on the left side, where relatively small observations pull the mean downward, and the mean is generally less than the median. Thus, skewness helps a data scientist identify whether observations are concentrated more heavily on one side of the centre and whether extreme values occur more frequently in one direction. Kurtosis measures the degree of tail heaviness and the concentration of observations around the centre of a distribution. It is commonly interpreted

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