What does the Poisson distribution primarily count?

The Poisson distribution is primarily used to model count data where the events occur independently, and it is particularly suited for counting occurrences of events in a fixed interval of time or space. This distribution is often applied in scenarios such as the number of defects found in a batch of products or the number of calls received at a call center in an hour, which makes it a powerful tool in quality control and process analysis.

When talking about defects, the Poisson distribution helps quantify the probability of a given number of defects occurring in a specific sample size or during a certain period. This is crucial in quality control, as organizations strive to minimize defects in their processes to enhance product quality and efficiency.

While defectives and continuous data are important concepts in quality processes, they are not specifically counted using the Poisson distribution. The distinction is vital; the Poisson distribution focuses on how many defects may be observed rather than categorizing the items themselves (defectives) or measuring quantities (continuous data).

In summary, the Poisson distribution's primary focus is on counting the number of defects in a defined framework, allowing analysts to forecast and manage quality effectively.