When comparing the mean with the median, which of the following is true in a skewed distribution?

In a skewed distribution, the mean is influenced by the presence of outliers and extreme values, which can distort its value. This occurs because the mean is calculated by summing all values in the dataset and dividing by the number of values, making it sensitive to very high or very low data points.

On the other hand, the median, which is the middle value when all data points are arranged in order, is not affected by outliers. It simply represents the point at which half the data points lie above and half lie below, providing a more robust measure of central tendency in the presence of skewness.

Thus, in skewed distributions, the mean can be significantly different from the median, which reinforces the idea that the mean is affected by outliers while the median remains unaffected. This distinction is crucial in data-driven decision-making when analyzing and interpreting data accurately.