Which explanation correctly contrasts joint and conditional probability?

Joint probability measures change over time between events A and B, whereas conditional probability measures stability across repeated samples irrespective of other events happening.

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.

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.

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.