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Understanding skewness and kurtosis is crucial in various fields, including statistics, economics, and social sciences. Skewness measures the asymmetry of a probability distribution, indicating whether the data is concentrated more on one side than the other. On the other hand, kurtosis measures the peakedness or flatness of a distribution, providing insights into the tails' behavior.

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Skewness indicates the asymmetry of a distribution. A skewness value of zero suggests a perfectly symmetrical distribution, where the mean, median, and mode are all equal. Positive skewness indicates that the tail of the distribution is longer on the right side, while negative skewness suggests a longer left tail.

On the other hand, kurtosis measures the peakedness or flatness of a distribution compared to a normal distribution. A kurtosis value of zero indicates the same peak as the normal distribution (mesokurtic), positive kurtosis indicates a higher peak (leptokurtic), and negative kurtosis indicates a flatter peak (platykurtic).

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