What does a negative skew indicate about a distribution of scores?

A negative skew in a distribution indicates that the tail on the left side of the distribution is longer or fatter than the right side. This suggests that the majority of the scores are concentrated toward the higher end of the scale, resulting in a situation where fewer lower scores pull the mean down. In this context, while the highest scores form the bulk of the distribution, the presence of a few lower scores, although not the majority, influences the overall shape of the curve.

Thus, the correct interpretation of a negative skew is that it reflects a predominance of higher scores, distinguishing it from other types of distributions that may indicate a more uniform or balanced arrangement of data points.