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A discrete distribution is defined by its handling of distinct values that can typically be counted or enumerated, such as the number of defective items, occurrences of a particular event, or the number of transactions. Thus, it fundamentally involves integer values since these represent countable quantities.

When considering the unique features of a discrete distribution, it is essential to note that it deals exclusively with integer values rather than fractional ones. For example, you can have 5 cars in a parking lot or 3 customers in a store, but you cannot have 2.5 cars or 3.7 customers.

Options emphasizing counted data and integer values align perfectly with the principles of discrete distributions. In contrast, the notion of allowing for fractional values fundamentally contradicts the essence of discrete distributions, which do not accommodate values that are not whole numbers. This distinction is crucial in understanding data types and their distributions within the framework of statistics and Six Sigma methodologies.