What is a characteristic of a normal distribution?

A normal distribution is defined by several key characteristics that collectively describe its shape and properties. One of the most recognizable features is its bell shape, which indicates that the data is symmetrically distributed around the mean. This symmetry means that the likelihood of values occurring is highest near the mean and tapers off as you move further away, resulting in the familiar bell curve appearance.

Additionally, in a normal distribution, the mean, median, and mode are equal. This equality signifies that the center of the distribution is not only the average but also the most frequent value, reinforcing the idea of balance within the distribution.

Another fundamental characteristic is that the total area under the curve, which represents the entirety of possible outcomes, sums to 1. This property is critical in probability theory, as it allows us to interpret the area under the curve as probabilities for intervals within the data.

Given these attributes—bell shape, equality of mean, median, and mode, and the total area equaling one—it is accurate to say that all these factors together fully encapsulate the characteristic of a normal distribution. Therefore, selecting the option that encompasses all these features recognizes the comprehensive nature of what defines a normal distribution.