<Tr> <Th> Fisher information </Th> <Td> <P> g n (p) = n p (1 − p) (\ displaystyle g_ (n) (p) = (\ frac (n) (p (1 - p)))) </P> (for fixed n (\ displaystyle n)) </Td> </Tr> <P> g n (p) = n p (1 − p) (\ displaystyle g_ (n) (p) = (\ frac (n) (p (1 - p)))) </P> <P> In probability theory and statistics, the binomial distribution with parameters n and p is the discrete probability distribution of the number of successes in a sequence of n independent experiments, each asking a yes--no question, and each with its own boolean - valued outcome: a random variable containing single bit of information: success / yes / true / one (with probability p) or failure / no / false / zero (with probability q = 1 − p). A single success / failure experiment is also called a Bernoulli trial or Bernoulli experiment and a sequence of outcomes is called a Bernoulli process; for a single trial, i.e., n = 1, the binomial distribution is a Bernoulli distribution . The binomial distribution is the basis for the popular binomial test of statistical significance . </P> <P> The binomial distribution is frequently used to model the number of successes in a sample of size n drawn with replacement from a population of size N. If the sampling is carried out without replacement, the draws are not independent and so the resulting distribution is a hypergeometric distribution, not a binomial one . However, for N much larger than n, the binomial distribution remains a good approximation, and is widely used . </P>

Describe what is meant by a binomial (or bernoulli) distribution
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