Which two statistical measures are insensitive to extreme scores in the distribution?

The median and quartile are both statistical measures that are not influenced by extreme scores, also known as outliers, in a data distribution.

The median represents the middle value when the data is ordered, which means that extreme values at either end of the distribution do not affect its calculated position. This makes the median a robust measure of central tendency, especially in skewed distributions where outliers may distort the average.

Quartiles divide a data set into four equal parts, with the first and third quartiles representing the values at the 25th and 75th percentiles, respectively. Since quartiles are determined based on the ranks of the data rather than their specific values, they too remain unaffected by extreme scores, providing a true representation of the distribution's spread.

In contrast, measures such as the mean are highly sensitive to outliers since it takes into account all values in the dataset. Therefore, in situations where data includes extreme scores, relying on the median and quartile can provide a clearer understanding of the central tendency and distribution of the data.