# Normal Distribution in Market Profile : Gaussian Distribution

Peter Steidlmayer developed the Market Profile based on the statistical normal distribution to identify value in any market. The normal distribution analysis is an important statistical study that is observed in many natural processes. It has a good concept called normal distribution in market profile.

It is a simple yet powerful statistical data distribution pattern. This distribution pattern occurs in almost every large group of data. What often seems to be a random variation in a given population of data ultimately tends to conform to a specific probability distribution. This statistical distribution is known as the normal distribution.

What often seems to be a random variation in a given population of data ultimately tends to conform to a specific probability distribution.

The normal distribution is the most commonly observed probability distribution in nature. If we examine the data for the age, IQ, height, weight, or eye color of any large group of individuals, we will find that when we tabulate the data that we collect about any of these variables, and the result will create a statistical normal distribution. In the late 1700s and early 1800s, renowned mathematicians like de Moivre and Laplace began to apply and use this statistical distribution method in their work. German mathematician and physicist Carl Gauss used it to analyze astronomical data and consequently, it is often referred to as the Gaussian distribution.

Gaussian or normal distribution is a symmetrical pattern with a single central peak at the mean or average of the data. The shape of the normal distribution curve is often described as the bell curve as a result of its characteristic bell-shape. The graph for the distribution tapers off evenly on either side of the mean. Fifty percent of the distribution lies to the left of the mean and fifty percent lies to the right of the mean. The spread of a normal distribution is controlled by the standard deviation. Standard deviation is a calculated measurement of variability or diversity that is used in statistical analysis and probability theory. The standard deviation shows us how much variation or “dispersion” there is away from the “average.” The smaller the standard deviation, the more concentrated the data.

A normal distribution can be completely specified by two key parameters, the mean and the standard deviation. The normal distribution also follows an empirical rule. The empirical rule is a common way to analyze data in the normal distribution. It provides us with a quick estimate of the spread of the data in the normal distribution. Once we have identified the mean and the standard deviation for the data set that is included in the normal distribution, the empirical rule basically states that:

- 68 percent of the data will fall within one standard deviation of the mean.
- 95 percent of the data will fall within two standard deviations of the mean.
- 99.7 percent of the data will fall within three standard deviations of the mean.

This empirical rule is extremely helpful in identifying or determining the probability of occurrence of a certain value in the normal distribution. The closer the value is to the mean, the greater the probability of its occurrence.