Uploaded January 2014 | Updated September 2026, 2 hours ago
A not-too-technical look at the conditions required for a random variable to have a Poisson distribution. It can be difficult to determine whether a random variable actually has a Poisson distribution, so here I look at a few examples and some visual illustrations that may help. There are no probability calculations carried out in this video. I assume that the viewer has already been introduced to the Poisson distribution, but I do a brief review at the start.
A not-too-technical look at the conditions required for a random variable to have a Poisson distribution. It can be difficult to determine whether a random variable actually has a Poisson distribution, so here I look at a few examples and some visual illustrations that may help. There are no probability calculations carried out in this video. I assume that the viewer has already been introduced to the Poisson distribution, but I do a brief review at the start.


![An Introduction to Hypothesis Testing
A first look at hypothesis testing.
For those that use R, below is the R code to find the binomial probability given in this video.
To find the probability that X takes on a value that is at least 28, where X has a binomial distribution with parameters n = 100 and p = 1/4:
1-pbinom(27,100,1/4)
[1] 0.2776195
To find the probability that X takes on a value that is at least 44, where X has a binomial distribution with parameters n = 100 and p = 1/4:
1-pbinom(43,100,1/4)
[1] 2.700295e-05 An Introduction to Hypothesis Testing](https://i.ytimg.com/vi/tTeMYuS87oU/mqdefault.jpg)







