First, the number of events occurring in a fixed interval of time. With the example, the "events" are the visits to the art gallery, and the selected fixed interval of time is one day. Second, the events occur with a known average rate. In this case, we can refer to historical data that presents on average 50 visitors visiting the gallery each day. Third is that events are independent of the time since the last event. Referring to the example, the arrival of a visitor is independent of the arrival of other visitors. One person's visit to the gallery does not affect the likelihood of other people visiting the gallery. By meeting the characteristics of a Poisson experiment, the scenario of predicting the visitor numbers to an art gallery in a day suits the Poisson distribution model. is this poison experiment