If the mean of a distribution is 23 and the median is 15.79, what can be inferred about the distribution?

When analyzing a distribution, the relationship between the mean and the median provides significant insight into its shape. In this case, the mean is 23 and the median is 15.79.

When the mean is greater than the median, it indicates that there are values within the dataset that are substantially higher than most of the other values. This typically suggests that the distribution has a longer tail on the right side. Such a situation results in a right skew, or positive skew, in which higher values pull the mean upward, while the median, being a measure of central tendency that is less affected by outliers, remains lower.

This contrast reveals that the bulk of the data lies below the mean. Therefore, the indication of a right skew is affirmed, as higher outlier values are influencing the average significantly more than they affect the median. This characteristic is typical in many datasets that have a few high values among a larger set of lower values, emphasizing the asymmetry in distribution.

In summary, given that the mean exceeds the median, it is accurate to infer that the distribution is skewed right.