What does a negative skewness indicate about a distribution?

A negative skewness in a distribution indicates that the left tail of the distribution is longer or fatter than the right tail, which is a characteristic of negatively skewed distributions. This means that there are a number of lower values that are pulling the mean towards the left side of the distribution.

In a negatively skewed distribution, the bulk of the data tends to accumulate towards the higher end (the right side), while the left tail extends further out with fewer extreme low values. This results in the mean being less than the median, which typically points to a concentration of data on the right (higher values), confirming the influence of those lower values in the tail.

The other options do not accurately describe the implications of negative skewness. For instance, a symmetric distribution would have a skewness of zero, while the relationship between the mean and median would not imply that the mean is greater in a negatively skewed distribution, but instead indicates otherwise. Hence, the primary takeaway is that a negative skewness reflects a longer left tail, affirming that the correct choice is focused on that characteristic.