What characterizes a negatively skewed distribution?

A negatively skewed distribution is characterized by a tail that extends to the left, meaning that the bulk of the data values are concentrated towards the higher end of the range. In this situation, the mean is typically less than the median because the presence of lower value outliers pulls the mean down. This characteristic implies that the mean is pulled to the left of the median, which aligns with the correct answer you identified.

The other options reflect different characteristics of distributions. For instance, stating that the mean is pulled to the right of the median suggests a positively skewed distribution, where the tail is on the right side, and thus does not apply here. The claim of all values being equally distributed describes a uniform distribution, not skewed at all. Lastly, saying that data is concentrated at the higher end describes the shape of the distribution but does not specifically link it to the relationship between the mean and median that defines skewness.