The normal distribution, often referred to as a bell curve, results from collecting what type of data?

The normal distribution, commonly known as a bell curve, arises from the characteristics of continuous data. Continuous data can take on an infinite number of values within a given range, making it suitable for capturing measurements that can vary to any degree of precision. Examples include height, weight, temperature, and age.

When a sufficient number of observations are collected, continuous data tends to cluster around the mean, resulting in the symmetrical, bell-shaped curve of the normal distribution. This curve reflects the properties of the probability distribution where most values are concentrated around the central peak, and the probabilities for values further from the mean taper off symmetrically in both directions.

In contrast, categorical data consists of distinct categories without a numerical value, discrete data includes countable values that can’t be divided into finer scales, and ordinal data represents ordered categories that may not have consistent intervals between them. These types do not yield the bell curve shape typical of a normal distribution. Thus, continuous data is the only data type that naturally leads to the formation of a normal distribution.