The negative binomial distribution is a probability distribution that models the number of trials needed to achieve a specified number of successes in a series of independent Bernoulli trials. Each trial has two possible outcomes: success or failure. This distribution is particularly useful in scenarios where the number of successes is fixed, but the number of trials can vary. In the negative binomial distribution, the probability of success remains constant across trials, and the trials continue until the desired number of successes is reached. It is often used in fields like statistics and biostatistics to analyze count data, especially when the data exhibit overdispersion, meaning the variance exceeds the mean.