The uniform distribution is limited to a finite domain, the normal is not.
Assuming that "piossion" refers to Poisson, they are simply different probability distributions that are applicable in different situations.
There is no such thing as "the usual sampling distribution". Different distributions of the original random variables will give different distributions for the difference between their means.There is no such thing as "the usual sampling distribution". Different distributions of the original random variables will give different distributions for the difference between their means.There is no such thing as "the usual sampling distribution". Different distributions of the original random variables will give different distributions for the difference between their means.There is no such thing as "the usual sampling distribution". Different distributions of the original random variables will give different distributions for the difference between their means.
They are continuous, symmetric.
What is the difference between dependant and independent events in terms of probability
probability is a guess and actuality is what will happen
The difference between "at least" and "at most" is not restricted to probability. The difference is simply one between the precise meaning of the phrases in every day English language.
None. The full name is the Probability Distribution Function (pdf).
A Bhattacharyya distance is another term for a Hellinger distance, a measure used to quantify the similarity between two probability distributions.
Proportion is the probability of a selected sample. probability is the true probability of all cases. If this is not what you are looking for then please specify.
They are the same. The full name is the Probability Distribution Function (pdf).
the difference is just that non-probability sampling does not involve random selection, but probability sampling does.
For a normal deck of cards, the answer is none.