What is the difference between discrete and continuous distributions?

• A. Continuous distributions model an infinite number of possible values within a range.
• B. Discrete distributions model countable outcomes; continuous distributions model an infinite number of possible values within a range.
• C. Both distributions model the same type of outcomes in most cases.
• D. Discrete distributions are always normal; continuous are uniform.

Answer: (B)

Difference between discrete and continuous distributions is that discrete distributions model countable outcomes; continuous distributions model an infinite number of possible values within a range. In discrete cases, you can list each possible value and assign a probability to it, with all those probabilities summing to 1. In continuous cases, there isn’t a separate probability for a single exact value—there are infinitely many values in a range, and probabilities are assigned to intervals via a density function, with probabilities derived from integrating that density over the interval.

This explains why the statement is correct: it captures the essential contrast in how outcomes are structured and how probabilities are represented for each type. The other options either describe only one side, claim they model the same outcomes, or assert rules that aren’t generally true (like discrete always being normal or continuous always being uniform).