Usefulness of the Poisson Distribution
A Poisson distribution can be used to explain the behaviour of a discrete random variable where the probability of happening of the events (p) is very small and the total number of possible cases (n) is indefinitely very large. As such, the Poisson distribution is extensively used in a variety of fields like, Physics, Biology, Business, Economics, Industries, Insurance and Waiting line problems etc. Most of the temporal distributions (that deal with the events that are supposed to occur in equal intervals of equal length along a straight way) follow the pattern of Poisson distribution. However, some practical cases in which the Poisson distribution is invariable used are cited here as under:
- It is used in the field of physics to find out the number of particles emitted from a radioactive substance.
- It is used in biology to count the number of bacteria in a unit cell.
- It is used in the statistical quality control to count the number of defects with a product.
- It is used in the queueing problems to determine the number casualties.
- It is used in the queueing problems to ascertain the number incoming customers.
- It is used in transport and assignment problems to count the number of traffic arrivals such as the trucks at terminals, the aeroplanes at airports, the ships at docks etc.
- It is used in printing presses to determine the number of typographical errors per page on a typed paper, or the number of printing mistakes per page in a book.
- It is used in birth and death registration offices to determine the number of deaths in a particular locality in a given period by a rare disease.
- It is used in telephone officers to count the number of telephone-calls arriving at a telephone switch-board per month.
- It is used to count the number of suicides reported in a particular day, or the number of casualty due to a rare disease such as heart attack, cancer, or snake bite in a year.
- It is used in counting the number of accidents taking place per day on a busy road.
- It is, also, used in modeling the distribution of number of persons waiting in a lien to receive some services.
