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Compound Probability

Middle & High School Depth 47 in the knowledge graph I know this Set as goal
875topics build on this
225prerequisites beneath it
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Simple ProbabilityIndependence and the Multiplication Rule
probability compound-events independent dependent

Core Idea

Compound probability deals with the likelihood of two or more events occurring together. For independent events (where one outcome does not affect the other), multiply the individual probabilities: P(A and B) = P(A) × P(B). Flipping heads then rolling a 6 gives (1/2)(1/6) = 1/12. For dependent events (where one outcome changes the available outcomes), the second probability must account for the change: drawing two aces from a deck without replacement gives (4/52)(3/51) = 12/2652 = 1/221. Distinguishing between independent and dependent events is the critical reasoning skill at this level.

How It's Best Learned

Use tree diagrams and organized lists to map all possible outcomes of two-event experiments. Start with clearly independent events (coin and die), then move to dependent events (drawing without replacement). Have students compare experimental results from simulations with their calculated probabilities to build intuition about why multiplication works.

Common Misconceptions

Explainer

You already know how to find the probability of a single event: count favorable outcomes, divide by total outcomes. Compound probability asks what happens when two events occur together or in sequence. The question that drives everything is: does the outcome of the first event change what's possible for the second?

When the answer is no, the events are independent. Flipping a coin and rolling a die are independent because the coin landing heads doesn't alter which faces the die can show. For independent events, the probability of both happening is found by multiplying: P(A and B) = P(A) × P(B). This rule has an intuitive reading — a 1/2 chance and a 1/6 chance together give (1/2)(1/6) = 1/12. Tree diagrams make this visible: each branch narrows the probability, and the final leaf is the product of all probabilities along the path to that outcome.

When the first event does change what's available for the second, the events are dependent. Drawing cards from a deck without replacement is the standard example. The probability the first card is an ace is 4/52. But if it is an ace, only 3 aces remain among the 51 remaining cards, so the probability the second card is also an ace is 3/51 — a different number. You must use the updated probability that reflects what happened first. The combined probability is (4/52) × (3/51) = 1/221. This is still multiplication — just with the second factor adjusted to reflect the changed situation after the first draw.

The critical skill is correctly classifying a pair of events before computing. Replacement is the usual signal: with replacement (or in genuinely separate experiments), events are independent; without replacement, drawing one item changes the pool and creates dependence. The most common error is assuming independence when events are actually dependent — which overestimates how likely joint events are, because favorable first outcomes deplete the pool for the second draw. When in doubt, ask: "After the first event, are the possibilities for the second event exactly the same as before?" If yes, multiply the original probabilities. If no, adjust the second probability first.

Practice Questions 5 questions

Prerequisite Chain

Understanding ZeroThe Number ZeroCounting to FiveCounting to 10Counting to 20Counting a Set of Objects Up to 20Cardinality: The Last Number CountedMatching Numerals to QuantitiesSubitizing Small QuantitiesAddition Within 10Making 10 as an Addition StrategyAddition Within 20Doubles and Near DoublesDoubles Facts Within 10Near Doubles Facts Within 20Mental Math Strategies for AdditionMental Math: Adding and Subtracting TensAddition Within 100Repeated Addition as MultiplicationMultiplication as Equal GroupsMultiplication: ArraysBasic Multiplication Facts Through 10Multiplication Facts Within 100Division as Equal SharingDivision as Grouping (Measurement Division)Division: Grouping (Repeated Subtraction) ModelDivision: Fair Sharing ModelDivision as Equal SharingDivision as GroupingBasic Division FactsDivision Facts Within 100Multiplication and Division Fact FamiliesRelationship Between Multiplication and DivisionDivision Facts as Inverse of MultiplicationRemainders and Quotients in DivisionDivision Word ProblemsMulti-Step Word ProblemsSolving Multi-Step Word ProblemsMultiplication Word ProblemsDivision Word ProblemsIntroduction to Long DivisionFactors and MultiplesPrime and Composite NumbersEquivalent FractionsMultiplying FractionsRatiosSimple ProbabilityCompound Probability

Longest path: 48 steps · 225 total prerequisite topics

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