Which set of statistical parameters is used to measure a distribution?

The set of statistical parameters that accurately measures a distribution includes the mean, variance, skewness, and kurtosis.

The mean provides a measure of central tendency, illustrating the average of the data points in the distribution. Variance indicates how spread out the values are around the mean, giving an insight into the variability of the data. Skewness assesses the asymmetry of the distribution, revealing whether the data leans towards one side (left or right) of the distribution. Lastly, kurtosis measures the "tailedness" of the probability distribution, indicating the presence of outliers and how peaked the distribution is compared to a normal distribution.

Together, these parameters offer a comprehensive overview of a distribution's shape, center, and spread, making them essential for effective statistical analysis.

Other options either include parameters that are not primarily used to quantify distribution properties (such as range or frequency) or do not include a complete set that captures both central tendency and the shape of the distribution effectively.