# DISTRIBUTIONS- POISSON AND NORMAL

Statistics is an important part of our life. No research work can take place without the use of statistics.One of the most important part of statistics is it’s distributions. They are known as statistical and probability distributions, for eg Â binomial, negative binomial, geometric etc. We all have heard about binomial distribution and have even learnt it at school level. But the two main distributions which are used in everyday life the most are Poisson distribution and normal distribution. The main purpose behind this article is to create awareness about these two because there is no research work without the knowledge of these two.

POISSON DISTRIBUTION

This distribution models the number of events that occur in a specified interval of time, when the events occur one after another in time in a well defined manner.For eg- events like modelling the number of goals scored in a match and its probability, modelling no. of accidents in a city, modelling no. of telephone calls in an hour in a call center etc.

There is an assumption that events occur at a constant rate and number of events that occur in separate time intervals are independent of each other.The Poisson distribution gives a good approximation to the binomial distribution.When number of events are very large ,i.e, n is very large in binomial then Poisson gives a good approximation to the answers.

NORMAL DISTRIBUTION

This distribution, with it’s symmetrical bell shape , is the most important distribution in both statistical and practice theory.It’s features include the following-

• it is bell shaped and symmetrical
• it is a ‘building block’ for many other distributions
• it is a good model for the distribution of measurement that occurs in practice in a wide variety of different situations. For eg, height of students.
• it provides good approximation to other distributions as well.
• much of large sample statistical inference is based on it, and some models require an assumption that the data belongs to normal distribution.

That was a brief introduction about these two distributions.The knowledge of these two is necessary to survive in the analytical and research sector today. I hope i was able to fulfill my objective for providing information about these two.