What are the characteristics of a normal distribution?

A normal distribution is characterized by its symmetrical, bell-shaped curve. This shape indicates that the data tends to cluster around the central peak, with values tapering off equally on both sides. One of the fundamental characteristics of a normal distribution is that the mean, median, and mode are all equal, which reflects the balance and symmetry of the distribution.

In a perfect normal distribution, the majority of the data points fall within a certain range around the mean, typically within one standard deviation. This reflects the concept where approximately 68% of data values lie within one standard deviation from the mean, about 95% within two standard deviations, and about 99.7% within three standard deviations, a concept known as the empirical rule.

The other choices mistakenly describe distributions that do not exhibit these characteristics. For example, a skewed distribution has a tail on one side, leading to the mean and median being different. A uniform distribution entails all outcomes being equally likely without the bell shape or central clustering. Bimodal distributions possess two peaks rather than a single symmetrical shape, indicating the presence of two different groups within the data. Hence, the correct attributes of a normal distribution directly align with the description provided in option C.