A Normal Distribution is also called **“Gaussian Distribution” **or more commonly known as “Bell Curve” as the probability distribution function plot of a normal distribution looks very like 🔔 bell-shaped.
A Normal Distribution is a univariate probability distribution_, _which means it is a distribution for only **_one _**random variable. Note: Multivariate normal distributions do exist but in this article, we would be talking about only univariate normal distribution.
The normal distribution is an arrangement of data points in which most values form a cluster in the middle of the range and the rest taper off symmetrically toward either extreme ends.
In probability theory, a normal distribution is a type of continuous probability distribution for a real-valued random variable (say X). The general form of its probability density function is
Normal distributions are very important in statistics and often they are very naturally occurring. One of the main reasons for the popularity of the Normal Distribution curve is that it occurs very commonly in most of the things we see in nature around us. For example: in finance, like the salary distribution in an office, healthcare, hydrology, **height/_weight _**distributions, **_grading _**distribution, **_Percentile _**calculations and much more. You name it and normal distribution owns it.
Why is the Bell Curve so famous?
If you have been in the field of **_statistics _**or machine learning or you have ever studied higher mathematics, then there is a very high chance that you have come across this term known as the **Normal Distribution **or Gaussian Distribution. For a very long time, normal distributions have made human life a lot simpler. Hard statistical calculations can be easily understood with the use of Normal Distribution curves which gives a very meaningful insight from its probability distribution function graph known as bell-curve.
Bell curves are “symmetric in nature” and have a great bell-like shape from the top till the bottom. The symmetricity of the Bell curve is very useful for understanding the **_nature _**of the data and **_how is it dispersed _**around. The data is distributed in a sense that most of it cluster around the mean and very unlikely happening events are at the ends. Secondly, the bell curve has the Mean, **Median **and **Mode **all three measures of central tendency lie at one single point which is the symmetric center of the curve.
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