Layman's terms to describe common discrete distributions I am currently taking a probability and statistics course. We recently started studying discrete random variables, specifically discrete distributions lately. I want to ensure I understand what each of these distributions enables one to compute. Are the following descriptions accurate?
$X\sim\textit{Bernoulli}\left(p\right)$, where $p$ is the probability of a success. The distribution solves the probability of a success in one trial.
$X\sim\textit{Geometric}\left(p\right)$, where $p$ is the probability of a success. The distribution solves the number of trials until a success.
$X\sim\textit{Binomial}\left(n,p\right)$, where $n$ is the number of trials and $p$ is the probability of a success. The distribution solves the number of successes in $n$ trials.
$X\sim\textit{Pascal}\left(m,p\right)$, where $m$ is the number of successes and $p$ is the probability of a success. The distribution solves the number of trials until $m$ successes.
$X\sim\textit{Poisson}\left(\lambda\right)$. I am having some trouble with this one. Could someone please explain?
Thank you!
 A: All correct, though I think the usual convention is that the Pascal distribution models the number of $failures$, rather than $trials$.  Of course these just differ by the constant $m$, so what you said is essentially the same - just make sure you use the right formula in each case:
$$
{\rm Pr}(X=k)={k-1 \choose m-1} (1-p)^{k-m}p^m,
$$
gives the probability that the $m^{{th}}$ success occurs on the $k^{th}$ trial.
$$
{\rm Pr}(X=k)={m+k-1 \choose m-1} (1-p)^{k}p^m,
$$
gives the probability that the $m^{{th}}$ success occurs after precisely $k$ failures.
As for the Poisson distribution, it is hard to describe its motivation in purely discrete terms, even though it is a discrete distribution.
The idea is that some process is going on continuously for a period of time $T$, and $\lambda$ is the success rate. So in any period of time ${\rm d}t$, you expect $\lambda\frac{{\rm d}t}T$ successes.
Then the Poisson distribution models the number of successes in the entire period.
A: As the other answer says Poisson distribution is counts that happen in a certain interval of time or space. It could be the number of algae filtered on a filtration system, or the number of errors printed in a page of a book. Whereas the other distributions you listed all have to do with trials of successes and failures, Poisson doesn't really involve this. However, it is true that a binomial distribution with large n is approximately Poisson. The mean $\lambda$ is the average number of counts you would expect for one occurrence of the Poisson random variable.
