What is the difference between normal distribution and Poisson distribution?
What is the difference between normal distribution and Poisson distribution?
The most commonly used type of probability distribution is the normal distribution (the Gaussian bell curve). It’s used for continuous data (data can take on an infinite number of values) like height, weight, etc. In contrast, the Poisson distribution works only for count data, where the data can take the values of non-negative integers only: gravida, number of falls, number of ischemic strokes, etc. These can’t be fractions or negative numbers.
Advantages and uses in research
- Simplicity: The Poisson distribution is mathematically simple and easy to apply
- Flexibility: It can be used in a variety of scenarios and research fields.
If you want further examples of how the Poisson distribution can be used, see how Yu et al. (2023) used Poisson distributions to model the lifetime incidence of cancer in people. Mubarik et al. (2023) similarly used a Poisson distribution to model breast cancer outcomes. Acuna-Hidalgo et al. (2017) also used a Poisson distribution in their study on the prevalence of clonal hematopoiesis-associated mutations.
