$h\left(x\right)=\frac{\left(\begin{array}{c}A\\ x\end{array}\right)\left(\begin{array}{c}N-A\\ n-x\end{array}\right)}{\left(\begin{array}{c}N\\ n\end{array}\right)}$

X ~ B(n, p) means that the discrete random variable X has a binomial probability distribution with n trials and probability of success p.

X = the number of successes in n independent trials

n = the number of independent trials

X takes on the values x = 0, 1, 2, 3, ..., n

p = the probability of a success for any trial

q = the probability of a failure for any trial

p + q = 1

q = 1 – p

The mean of X is μ = np. The standard deviation of X is σ = $\sqrt{npq}$.

where P(X) is the probability of X successes in n trials when the probability of a success in ANY ONE TRIAL is p.

$P(X=x)=p(1-p{)}^{x-1}$

X ~ G(p) means that the discrete random variable X has a geometric probability distribution with probability of success in a single trial p.

X = the number of independent trials until the first success

X takes on the values x = 1, 2, 3, ...

p = the probability of a success for any trial

q = the probability of a failure for any trial p + q = 1
q = 1 – p

The mean is μ = $\frac{1}{p}$.

The standard deviation is σ = $\sqrt{\frac{1–p}{p^2}}$ = $\sqrt{\frac{1}{p}\left(\frac{1}{p}-1\right)}$ .

X ~ P(μ) means that X has a Poisson probability distribution where X = the number of occurrences in the interval of interest.

X takes on the values x = 0, 1, 2, 3, ...

The mean μ or λ is typically given.

The variance is σ² = μ, and the standard deviation is
$\sigma \text{ = }\sqrt{\mu }$.

The probability of having exactly $x$ successes in $r$ trials is $P\left(X=x\right)=\left(e^{-\mu }\right)\frac{{\mu }^x}{x!}$.

When P(μ) is used to approximate a binomial distribution, μ = np where n represents the number of independent trials and p represents the probability of success in a single trial.