In probability theory and statistics, the **Poisson distribution**, named after French mathematician Denis Poisson, is a discrete probability distribution that expresses the probability of a given number of events occurring in the rationale of a fixed interval of time or space if these events occur with a known constant mean rate and independently of the time since the last event.^{} The Poisson distribution can also be used for the number of events in other specified intervals such as distance, area or volume. A more simplified definition can be that it is a probability function that is used for charts for defects.

For instance, a call center receives an average of 180 calls per hour, 24 hours a day. The calls are independent; receiving one does not change the probability of when the next one will arrive. The number of calls received during any minute has a Poisson probability distribution: the most likely numbers are 2 and 3 but 1 and 4 are also likely and there is a small probability of it being as low as zero and a very small probability it could be 10. Another example is the number of decay events that occur from a radioactive source during a defined observation period.

#### References

Wikipedia. Poisson distribution. https://en.wikipedia.org/wiki/Poisson_distribution