Binomial probability distribution is a form of discreate probability distribution that is associated with a multiple step experiment called the binomial experiment. The binomial experiment is imperative to understand the probability distribution. There are four properties in the binomial experiment which are it must consist of a sequence of identical trials, two outcomes possible per trial (outcomes referred to as success or failure), the probability of success/failure does not change from trial to trail, and the trials are independent (Anderson et al, 2020). The main interest that one is looking for in the binomial experiment is the number of successes occurring in the trails. In this type of experiment the number of success (x) can assume infinite values making it a discreate random variable. Therefore, the probability associated with this random variable is binomial probability distribution. A binomial experiment that was done in the healthcare industry was seeing the efferences of electronic health records storage in a cloud environment. The experiment created a EHR (electronic health record) to see if the binary binomial tree like data structure and models would result is a secure and efficient way over the existing paper chart strategies (Mishra et al., 2019). The experiment provided data to help move the current charting system into a way that was deemed more efficient in the long run.

Poisson probably distribution is another form of discreated probability distribution that is associated with an experiment that occurs over a specified interval of time or space known as the Poisson experiment. This experiment consists of two properties which are the probability of an occurrence is the same for any two intervals of equal length and the occurrence or nonoccurrence is any interval independent of the occurrences or nonoccurrence in any other interval (Anderson et al, 2020). The Poisson probability distribution is a discreate random variable indicating the number of occurrences in the interval. This variable like the binomial experiment can equal an infinite number. A Poisson probability function experiment that happened in the healthcare industry was when it was used to help optimize trauma operating room availability. The arrival of patient at a trauma center is a random event that follows the Poisson probability distribution as the likelihood of patients requiring immediate surgery is similar in any two time periods of equal length and the arrival or non-arrival of patients is independent of the arrival or non-arrival in any other period of time (Sinclair, 2010).

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