What does a negative skewness indicate about a data distribution?

A negative skewness indicates that the distribution of data is asymmetric and tails off to the left side. This means that the majority of data values are concentrated on the right side of the distribution. Consequently, a negative skewness typically results in the mean being less than the median, as the outliers on the left side pull the mean down. Therefore, the correct understanding is that the bulk of data values being on the right signifies that fewer extreme values are found on the left end of the distribution, emphasizing the concentration of data points toward the higher values on the right.

Understanding skewness is crucial in statistics because it helps describe the shape of the data distribution. Knowing whether data is negatively or positively skewed can inform statistical analyses and the measures of central tendency that are most appropriate to use. In the case of negative skewness, recognizing that the bulk of data values are on the right enables more accurate interpretations of the distribution's characteristics.