Which type of distribution indicates that values are more likely to fall near the average?

The normal (Gaussian) distribution is characterized by its symmetric bell-shaped curve, which indicates that the values are more likely to cluster around the mean (average) of the data set. In this type of distribution, approximately 68% of the values fall within one standard deviation of the mean, and around 95% fall within two standard deviations. This property signifies that as you approach the average, the likelihood of encountering values increases, leading to fewer occurrences as you move away from the mean in either direction.

In contrast, the other types of distributions do not exhibit this characteristic clustering around the average. The rectangular (uniform) distribution has values that are evenly spread across a range, implying no particular tendency for values to cluster near a central point. The bimodal distribution features two distinct peaks, indicating that there are two locations where values tend to cluster, rather than one central average. Lastly, the exponential distribution has a tendency to decrease rapidly, with most values clustering closer to zero and fewer values appearing as you move away. Each of these distributions has distinct characteristics that differentiate them from the normal distribution's defining trait of proximity to the average.