What is a defining feature of skewed distributions?

Skewed distributions are characterized by the fact that they have one tail longer than the other. This asymmetry indicates a departure from the symmetry seen in normal distributions. In a skewed distribution, the direction of the skew (left or right) indicates where the bulk of the data lies and where the tail extends. For example, in a positively skewed distribution, the tail on the right side is longer, indicating that there are a number of higher values stretching the distribution outward. Conversely, in a negatively skewed distribution, the tail on the left side is longer, showing that lower values are extending the distribution downward. This feature is significant because it can impact statistical analyses, such as the calculation of the mean, median, and mode, as well as influence the interpretation of data trends.

The other options do not accurately describe skewed distributions; for instance, they being symmetric, depicting normal probabilities, or involving mutually exclusive events do not apply to the key characteristic of skewness that emphasizes the unequal tail lengths.