How can a normal distribution best be described?

A normal distribution can be best described by several key characteristics that align closely with the chosen answer. It is primarily recognized for its bell-shaped curve, which illustrates how data points are distributed around the mean. This bell shape signifies that most values cluster around the mean, with frequencies tapering off symmetrically as you move away from the center.

In a normal distribution, the data is symmetrical about the mean, meaning that the left side of the graph is a mirror image of the right side. This symmetry implies that the mean, median, and mode all occur at the same point, but the answer emphasizes the concept of having a "single mode," which is a typical characteristic of normal distributions. The presence of only one peak (or mode) underscores the idea that there is one most common value, which is the average or central point of the data set.

As for the other choices, while some aspects may touch on features of distributions, they don't accurately capture the essence of a normal distribution. For instance, references to variance or discrete data do not appropriately describe the continuous nature of normal distributions. These key features are that they are continuous, bell-shaped, and symmetrical, making the distinction clear and significant when identifying a normal distribution in statistical contexts.