Consider an experiment having two possible outcomes:
either success or failure. Suppose the experiment is repeated
several times and the repetitions are independent of each
other.
The total number of experiments where the outcome turns
out to be a success is a random variable whose distribution is
called binomial distribution. The distribution has two parameters:
the number of repetitions of the experiment and
the probability of success of an individual
experiment.
A binomial distribution can be seen as a sum of
mutually independent Bernoulli random variables that take value 1
in case of success of the experiment and value 0
otherwise.
This connection between the binomial and Bernoulli
distributions will be illustrated in detail in the remainder of
this lecture and will be used to prove several properties of the
binomial distribution.