The Poisson Distribution

When one expects to find on average one number of randomly distributed objects or occurances of something in a given area, volume, measure of time, etc..., and one wants to find the probability of seeing exactly $X$ objects/occurances (which may be different than the expected number), the Poisson Distribution is used.

If the expected number of objects/occurances described above is denoted by the Greek letter, lambda, then the probability of seeing exactly $X$ objects/occurances is given by

$$P(X) = \frac{e^{-\lambda} \lambda^X}{X!}$$

There are many examples of when using the Poisson distribution might be appropriate:

  • The number of cars that pass through a certain point on a road (sufficiently distant from traffic lights) during a given period of time.
  • The number of spelling mistakes one makes while typing a single page.
  • The number of phone calls at a call center per minute.
  • The number of times a web server is accessed per minute.
  • The number of roadkill (animals killed) found per unit length of road.
  • The number of mutations in a given stretch of DNA after a certain amount of radiation.
  • The number of pine trees per unit area of mixed forest.
  • The number of stars in a given volume of space.
  • The number of soldiers killed by horse-kicks each year in each corps in the Prussian cavalry. This example was made famous by a book of Ladislaus Josephovich Bortkiewicz (1868–1931).
  • The number of light bulbs that burn out in a certain amount of time.
  • The number of viruses that can infect a cell in cell culture.
  • The number of inventions invented over a span of time in an inventor's career.
  • The number of particles that "scatter" off of a target in a nuclear or high energy physics experiment.