What characterizes a normal distribution?

A normal distribution is characterized by its symmetric, bell-shaped curve, which indicates that the majority of the data points are concentrated around the mean. This clustering around the mean is a key feature, as it reflects that values closer to the mean are more common, while values further away become increasingly rare. The tails of the distribution approach, but never actually touch, the horizontal axis, which means that extreme values (both high and low) become less probable.

Additionally, in a normal distribution, the mean, median, and mode are all located at the center, further emphasizing the symmetry. This characteristic is fundamental in statistics, as many statistical tests and methods assume the data follow a normal distribution, allowing researchers to make inferences that rely on this predictable pattern. The presence of this clustering and symmetry distinguishes the normal distribution from other types, such as skewed or uniform distributions.