When data follows a skewed distribution with a tail pulled toward the right side, this is referred to as:

In a scenario where data exhibits a skewed distribution with a tail extending toward the right side, this is best described as a positively skewed distribution. In such distributions, the majority of the data points remain clustered toward the lower end of the scale, while a few surrounding the higher end stretch the tail out. This can often result from infrequent high values that are significantly larger than the rest of the data, thereby pulling the mean to the right of the median.

In contrast, a negatively skewed distribution would display a long tail on the left side, indicating that the majority of data lies on the higher end. A normal distribution, on the other hand, has a symmetrical bell shape where the mean and median are equal. Finally, an outlier distribution refers generally to a dataset where one or more data points differ significantly from other observations. While outliers can exist in any distribution, the term does not define the overall shape of the distribution itself. Therefore, the characterization of a distribution with a rightward tail as positively skewed is accurate.