Your SIGNIFICANCE OF NORMAL DISTRIBUTION Normal distribution is a theoretical, at the same time a mathematical model of frequency distribution of a set of variable data. A bell-shaped symmetrical curve on the mean, basically represents this model and is also called the Gaussian distribution – Gauss. Gauss is a a German mathematician and astronomer. In 1807, Gauss proposed a principle of calculating an appropriate error distribution, which is now called normal or Gaussian distribution. In a normal distribution, the model’s characteristics are exactly determined and regardless of biases from the population with which it is taken, approximate statistics will show on the sampling distributions. These properties permit the normal distribution to be applied as the basis for estimating how huge or small sampling errors are. The normal distribution or normal curve is one of a biggest number of probable distributions. it has a standard deviation of 1 and a mean of 0.

In most cases, it is not feasible to gather data on the whole target population. Supposed an entrepreneur plans to invest a shopping mall in a certain locality and decides to sell more clothings. He might be interested to know the body sizes of the people within the perimeter from the store, however, finds it impossible to collect all the data about the residents. Then, if the data subset or sample size of the population of interest can be considered instead of including the entire population. Hence, repeating the data gathering procedure would most likely lead to a different group of numbers. A framework or representation of the distribution is used to provide some sort of consistency to the results.

Using normal distribution is very important since it provide appropriate description about the measures of the variables (height, weight, age, economic profile, reading ability, job satisfaction, work performance, memory, life span and many others) precisely and normally distributed. The normal distribution can be effectively used to describe with accuracy, any factor or variable which tends to clump or agglomerate surrounding the mean. For example, the heights of children ages 6 in the United States are just about to be normally distributed, with a mean of about 4 ft. Most children have a height which is close to the mean, although there is a small number of "outliers" in which height is significantly higher or below the mean. Through the use of a histogram, heights of children will appear the same as a bell curve, with the correspondence that is becoming closer if and when more data is used.

In addition, normal distribution is so significant due to the fact that it is so easy to prepare and work with it. The approximations are beneficial and faster to estimate or compute by hand. Students can do it by themselves with minimum instruction, besides, several types of statistical tools can be derived out of using normal distributions. Fortunately, these tests derived from normal distribution work very well although the distribution is only more or less normally distributed.