Difference between normal distribution and multinomial distribution I would like to ask the difference between the normal distribution and the multinomial distribution because I don't know when to use each of them.
I know the normal distribution is used for continuous probability, and the multinomial distribution is used for probabilities of K kinds of categories.
Can anyone give me some examples of each to make me understand them more clearly?
Thanks.
 A: The Wikipedia page on Distributions has a list of many common distributions along with short descriptions and links to more details for each of the listed distributions.  Perusing that list may help.  There are a lot more distributions than just the 2 that you mentioned.
The multinomial is used when you have a finite number (usually small) of classes/groups where ordering does not matter.  It can be used to describe things like favorite color on a survey, or the type of car that passes a given point next.
The normal distribution is used for continuous data, values that theoretically can take on an infinite number of values if we measured precisely enough (though in practice we will round to a finite subset).  Things that result as a combination of many small effects tend to work well with the normal distribution, for example heights in a homogeneous population are determined by several genetic and environmental factors and tend to be reasonably close to normally distributed.  Sample means are also a commonly treated as normally distributed (or at least close enough).  
