How does a hypergeometric distribution differ from binomial distribution?

The hypergeometric distribution is distinct from the binomial distribution primarily due to its sampling method, which is conducted without replacement. In a hypergeometric scenario, each selection from the population decreases the number of items available for subsequent selections, which significantly affects the probabilities of outcomes. This is particularly important in finite populations where the total number of items is fixed.

In contrast, the binomial distribution applies to scenarios where each trial is independent, and the sampling is conducted with replacement. That is, the probability of success remains constant throughout the trials because each trial involves the same total population size.

This fundamental difference in sampling method is what appropriately characterizes the hypergeometric distribution. Other options do not correctly describe this relationship or the inherent features of either distribution, such as the continuous data requirement or uniform probabilities, which are not specific characteristics relevant to distinguishing between these two distributions.