What is the binomial distribution and when should I use it?

In statistics, we use the binomial distribution to model situations when we take an experiment (like rolling a die) and we repeat that experiment a certain number of times. Importantly, within each experiment we designate a certain event to be considered a "success", and if that event doesn't happen, we call it a "failure". We say the random variable which represents the number of successes is a binomially distributed random variable.For example, let's take our situation to be rolling a die 10 times; we will consider it a "success" if we roll a 6, and a "failure" otherwise. Then, since we have a fixed probability of rolling a 6 (namely 1/6), we can use the formula for binomially distributed random variables to find the probability that we roll a 6 exactly twice is equal to approximately 0.29 (a derivation of this is preferably in-interview).Critically, we use the properties of a binomial random variable only if it satisfies the following 4 criteria:1) There is a fixed number of trials,2) There are two outcomes (success or failure),3) The outcome of each trial is independent of that of the other trials which have taken place,4) The probabilities of success and failure remain constant from trial to trial.For a binomial experiment on 'n' trials with a probability of success 'p', the number of successes is a binomial random variable with parameters n, p.