# Statistics – Difference between Poisson and Binomial distributions

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# Difference between Poisson and Binomial distributions. [closed]

If both the Poisson and Binomial distribution are discrete, then why do we need two different distributions?

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The difference between the two is that while both measure the number of certain random events (or “successes”) within a certain frame, the Binomial is based on discrete events, while the Poisson is based on continuous events. That is, with a binomial distribution you have a certain number, $n$, of “attempts,” each of which has probability of success $p$. With a Poisson distribution, you essentially have infinite attempts, with infinitesimal chance of success. That is, given a Binomial distribution with some $n,p$, if you let $n\rightarrow\infty$ and $p\rightarrow0$ in such a way that $np\rightarrow\lambda$, then that distribution approaches a Poisson distribution with parameter $\lambda$.
Because of this limiting effect, Poisson distributions are used to model occurences of events that could happen a very large number of times, but happen rarely. That is, they are used in situations that would be more properly represented by a Binomial distribution with a very large $n$ and small $p$, especially when the exact values of $n$ and $p$ are unknown. (Historically, the number of wrongful criminal convictions in a country)