What describes a Poisson probability distribution?

The Poisson probability distribution is fundamentally characterized by its ability to model the probability of a given number of events occurring in a fixed interval of time or space. Specifically, it provides the likelihood of observing a specific number of occurrences (x) of an event, assuming these events happen with a known constant mean rate and independently of the time since the last event occurred. This makes option B accurate as it directly addresses the primary purpose of a Poisson distribution.

Additionally, while other options might touch on aspects associated with probability distributions in general, they do not encapsulate the essence of the Poisson distribution itself. For example, it is not only applicable to continuous data (which option A wrongly implies); rather, it specifically handles the count of discrete events. Furthermore, although the Poisson distribution can relate to fixed intervals (as mentioned in option C), that alone doesn’t describe its core functionality, which centers on the probabilities of occurrences. Lastly, option D suggests calculating average outcomes, which is more aligned with other distribution types and does not pinpoint the unique characteristics of the Poisson distribution. Thus, option B is the most accurate representation of this statistical concept.