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2.5 Joint Probability and Disjoint Events

Can Both Things Be True?

A selected transit ticket can be a student ticket and a weekday ticket at the same time. The intersection A∩BA\cap B contains those shared outcomes. Its probability, P(A∩B)P(A\cap B), is the joint probability—the chance that both events occur. The notation P(A intersect B)P(A\text{ intersect }B) means the same thing.

Consider a complete set of 200 tickets, each equally likely to be selected. Each ticket has one student-status label and one weekday-status label.

Weekday BNot weekdayTotal
Student A602080
Not student8040120
Total14060200

The overlap contains 60 tickets, so P(A∩B)=60/200=0.30P(A\cap B)=60/200=0.30. This is not P(A)P(A) or P(B)P(B): the numerator must satisfy both descriptions. Thirty percent of this ticket population has both labels.

Worked Contrast: One Assigned Route

Now let RR mean “a ticket is assigned the Red route” and GG mean “it is assigned the Green route.” Suppose each ticket has exactly one route assignment. No ticket belongs to both route categories. Thus R∩G=∅R\cap G=\varnothing, and P(R∩G)=0P(R\cap G)=0. The events are mutually exclusive, also called disjoint: they cannot happen together on the same selected ticket.

Compare that with student and weekday status. Their positive joint probability proves they are not disjoint. A useful justification names the observational trial, checks whether shared outcomes exist, and connects that overlap to joint probability. Simply calling the labels “different” is insufficient; different attributes can overlap.

For finite equally likely ticket outcomes, zero shared tickets means zero joint probability. The set statement R∩G=∅R\cap G=\varnothing and the numerical statement P(R∩G)=0P(R\cap G)=0 describe different objects. Keep the set and probability notation distinct. Also keep disjoint separate from independent: whether events can occur together is a different question from whether learning one event changes the probability of the other.

When reading a table or diagram, point to the overlap explicitly. In the table, it is the 60 in the student/weekday cell. Adding row and column totals would double-count that overlap rather than calculate a joint probability.

Try It Yourself

The following original practice applies the ideas above to a fresh setting. Show the reasoning requested in each part. Answers and task-specific rubrics are on the separate solutions page.

Original generated practice for the Fall 2026 course.

Which Locker-Code Events Are Disjoint?

A locker-code generator selects one integer uniformly from 1 through 10. Let A={1,2,3}, B={8,9,10}, C={2,4,6,8,10}, and D={8,9,10}. A trial is one selected code; every code has positive probability 1/10.

Part a

Calculate P(A intersect B), also writing the probability with the intersection symbol. Justify whether A and B are mutually exclusive using both possible joint outcomes and joint probability.

Part b

Calculate P(C intersect D). Explain why this is a joint probability and justify whether C and D are disjoint. Distinguish the intersection set from its numerical probability.

Worked answer and rubric

Glossary and deeper reading · 4 refreshers

These editorial refreshers supplement the reading. Definitions and examples are added guidance; original source wording and recorded answers remain in their original context.

  • Probability

    Probability quantifies how likely an event is under a specified chance model. It lies between 0 and 1, with larger values representing greater likelihood. For example, drawing a red card from a well-shuffled standard deck has probability one half.

    Read more: Probability in the captured Statistics reading →

  • Event in probability

    An event is a set of outcomes from a chance process’s sample space. The event occurs when the observed outcome belongs to that set. For example, rolling an even result is the event containing 2, 4, and 6 for a six-sided die.

    Read more: Event in probability in the captured Statistics reading →

  • Mutually exclusive events

    Mutually exclusive events cannot occur together in the same trial. Their intersection is empty, so the probability that either occurs is the sum of their probabilities. For example, rolling exactly 1 and rolling exactly 4 are mutually exclusive outcomes of one die roll.

    Read more: Mutually exclusive events in the captured Statistics reading →

  • Statistical independence

    Statistical independence means that learning one outcome does not change the probability distribution of another. For two events, it implies that the probability of both occurring equals the product of their probabilities. For example, independent draws with replacement keep the second draw’s color probabilities unchanged by the first.

    Read more: Statistical independence in the captured Statistics reading →

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