
The distribution of successes in a fixed number of yes-or-no trials.The binomial distribution describes the number of successes in a fixed number of independent yes-or-no trials, each with the same probability of success. For example, flipping a coin 10 times and counting heads follows a binomial distribution with parameters n=10 and p=0.5. It is one of the most fundamental discrete probability distributions, serving as the basis for many statistical tests and models.
The distribution was introduced by Jacob Bernoulli in his 1713 work 'Ars Conjectandi', where he proved the first version of the law of large numbers.
The probability of getting exactly k successes in n trials is given by the formula P(k) = C(n,k) * p^k * (1-p)^(n-k), where C(n,k) is the number of ways to choose k successes from n trials. The distribution is characterized by its mean (n*p) and variance (n*p*(1-p)). As n grows large, the binomial distribution approaches a normal distribution, a key result used in many approximations.
It is used in quality control (e.g., number of defective items in a batch), clinical trials (e.g., number of patients responding to a drug), and A/B testing in web analytics.
The binomial coefficient C(n,k) appears in Pascal's triangle, and the distribution's shape can be symmetric, skewed, or even U-shaped depending on p.






