# Connection between the Binomial distribution, Poisson distribution and Normal distribution

I am trying to understand intuitively the connection between the 3. Here's what I think I understand so far

1. I have read that the normal distribution can approximate the other two
2. The Poisson distribution is the limiting case where the number of "trials" goes to infinity while the individual trial probability goes to zero much like how the formula for continuous compound interest is formed
3. The normal distribution works as a good approximation when the number of trials is quite large

These 2 distributions, Poisson and binomial are clearly related, but I don't understand why taking a larger number of samples would affect the approximation using a normal distribution, or even why the binomial distribution is related to the normal distribution at all. Can someone help me with some intuition? (using limits would help for large number of trials n)

• Look up the Central Limit Theorem. Turns out the normal distribution can approximate the distribution of average independent samples from any distribution... Mar 3 '19 at 5:49
• As the binomial distribution can be seen as summing Bernoulli trials it is s consequence of the central limit theorem that it is related to the normal .
– Karl
Mar 3 '19 at 5:49

The binomial distribution is symmetrical when $$p$$, the probability of success, is 0.5, so when the binomial's $$p$$ does not stray too far away from 0.5, the binomial distribution can be modelled by the symmetrical Normal distribution.
The binomial distribution is skewed when $$p$$ is close to 0 or 1, so when the binomial's $$p$$ has such values. say below 0.1 or above 0.9, it can be modelled by the heavily skewed Poisson distribution.