Which explanation correctly contrasts joint and conditional probability?

• A. Joint probability measures change over time between events A and B, whereas conditional probability measures stability across repeated samples irrespective of other events happening.
• B. Joint probability refers to the sum of probabilities for separate events, and conditional probability is the average probability across all outcomes once a subset is removed arbitrarily.
• C. Joint probability is always larger than conditional probability because it counts more events, while conditional probability is smaller since it conditions on a known event.
• D. Joint probability is P(A and B), the probability both occur simultaneously; conditional probability is P(B | A), the probability B occurs when A is known to have occurred.

Answer: (D)