Normal Distribution

Normal distribution, often called a “bell curve,” is a way to show how data points are spread out. This pattern pops up a lot in various kinds of data, especially when you’re looking at things like test scores, heights, or even errors in manufacturing. The graph of a normal distribution is shaped like a bell: most of the data clusters around the middle, which is the average or mean, and then the data points taper off symmetrically on both sides as they get farther from the average.

In business intelligence and data analytics, understanding normal distribution is crucial. It tells a business that most results or observations are going to fall close to the middle range. For example, if a shoe company knows that most people have a foot size that’s pretty close to the average, they’ll make more shoes in that size and fewer in the very small or very large sizes.

Knowing about this distribution helps businesses make predictions about what to expect from their data. If something they measure, like customer wait times, follows a normal distribution, they can make informed decisions about how to manage resources to improve service, among other things. It’s a fundamental concept in making data-driven decisions that help companies operate efficiently and effectively.