An example of this would be when we want to know the number of possible defective products contained in a large consignment from a manufacturing company. The given probability of any particular individual item/product being defective is then considered to be very small, let's say 0.01 percent. If we had a large number of items/products in the consignment such as 10000, we can then approximate the binomial distribution with the use Poisson distribution. By using the Poisson distribution equation, we are then able to calculate the probability of a specific number possibly being defective within the consignment, this is because we are given the known average rate of defective items/products (Illowsky, Dean, Birmajer & et al. 2022). This approximation is then valid due to the number of trials (items/products) being large and the probability of success (defective item/product) being very small (Illowsky, Dean, Birmajer & et al. 2022). In summary, the Poisson distribution can be used to model that of a Poisson experiment where the given events occur with fixed intervals of time or space, we have a known average rate, and each event is independent of another with no bearing. We can also use it to help us approximate the binomial distribution when our given number of trials (products) is too large and our probability of success is deemed too small. Comments please