What type of distribution is modeled by fixed independent trials resulting in two outcomes?

The binomial distribution is modeled by fixed independent trials that produce two possible outcomes, often referred to as "success" and "failure." This distribution is particularly applicable when the number of trials is predetermined and each trial has the same probability of success. For example, flipping a coin a certain number of times produces either heads or tails, which fits the criteria for a binomial experiment.

In a binomial distribution, the key factors include the number of trials (n), the probability of success in a single trial (p), and the probability of failure, which is equal to (1 - p). This distribution is effectively used in various fields such as quality control, medical trials, and survey analysis, where outcomes are dichotomous.

In contrast, other distributions mentioned do not share the same characteristics. The normal distribution involves continuous data and is not limited to two outcomes. The exponential distribution models the time until an event occurs and does not deal with fixed trials or binary outcomes. The Poisson distribution, while also dealing with count-based occurrences, models the number of events that happen in a fixed interval of time or space rather than fixed trials with two outcomes. Therefore, the binomial distribution is the most appropriate choice for the scenario described.