What describes a positively skewed distribution?

A positively skewed distribution is characterized by a long tail on the right side of the cluster of data. This means that most of the data points are concentrated on the left side, with fewer data points trailing off towards the higher values on the right. In such distributions, the mean is typically greater than the median due to the influence of the larger values within the tail. This kind of skewness can often be observed in various real-world scenarios where a limited number of outlier values significantly impact the average while the majority of the data falls at lower ranges.

The other options do not accurately capture the defining feature of a positively skewed distribution. For instance, a symmetric shape depicts a balanced distribution where the mean and median are the same, while a scenario where all data points are equal suggests no variation at all, leading to neither skewness nor clustering. Lastly, a distribution with a long tail on the left represents a negatively skewed distribution, where more values lie on the higher end, countering the characteristics of positive skewness.