Poisson Distribution differs significantly from Binomial and Hypergeometric Distributions which also measure probabilities associated with discrete outcomes, but rely on different formulas for calculating said probabilities due to differences in their assumptions about sample sizes and potential outcomes versus trials/observations/experiments performed respectively. The Poisson Distribution tends to be used more often when there is no known upper limit to the number of occurrences involved in the process being studied; however, it can still produce reliable estimates even with these constraints present if need be. When should Poisson Distribution be used? A good rule of thumb for determining whether or not this particular type of analysis might be beneficial for addressing a research question or exploring data set composition involves asking: “Given my particular independent variable(s), what are the chances that I observe X occurrence at any given point in time?” If you have answered yes to this question then using Poisson Distribution may help you draw valid inferences regarding your data set composition! An example visualization of what Poisson distribtion looks like can be found here: "https://www2.statisticshowto.datasciencecentral.com/probability-and-statistics/poisson-distribution/" This graph illustrates how extreme values become increasingly unlikely as we move away from μ = 0 towards either positive infinity (right side) or negative infinity (left side).

Sample Solution

Normal distribution and Poisson distribution are two common types of probability distributions. Normal distribution is a continuous, bell-shaped curve that describes how individual scores on variables (e.g., height) tend to cluster around the average or mean score of the sample population (i.e., most people have an average height). In contrast, Poisson distribution is a discrete probability distribution that indicates the probability of a certain number of events occurring within a specified time period. It is most commonly used when measuring rare events such as traffic accidents or phone calls received in an office.