The Poisson probability distribution is a probability distribution that is used to model the number of activities that are happening at a particular time, given that we know how often it usually happens and assuming that each event is independent of the other. One example of how the Poisson distribution can be applied in real life is in the context of art gallery visitors on a given day. An area of interest I selected for this week's discussion forum. Modeling the number of visitors to an art gallery using the Poisson distribution helps to understand, analyze, and make predictions about the behavior being studied. 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. Now, how to explain how the Poisson distribution can approximate the binomial distribution