Which term describes a distribution that is bell-shaped and shows how data is spread?

The term that describes a distribution that is bell-shaped and shows how data is spread is known as a normal distribution. This concept is fundamental in statistics and helps in understanding how data points are organized around a central mean value. In a normal distribution, the majority of the data points cluster around the mean, and the probabilities for values farther away from the mean taper off symmetrically. This bell-shaped curve is characterized by its mean and standard deviation, which indicate where the center of the distribution lies and how spread out the data points are, respectively.

Normal distributions are important in statistics because many statistical methods, including hypothesis testing and various forms of regression analysis, assume that the underlying data is normally distributed. This makes it easier to apply statistical models and draw conclusions based on the data.

In contrast, the other terms mentioned refer to different concepts: standard deviation is a measure of the dispersion of data points around the mean, variance quantifies the degree of spread in the data by squaring the standard deviation, and quartiles are values that divide the dataset into four equal parts. While these terms are related to data analysis and understanding distributions, they do not define a bell-shaped curve specifically.