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NotesMath AI HLTopic 4.6Tree Diagrams and Conditional Probability
Back to Math AI HL Topics
4.6.23 min read

Tree Diagrams and Conditional Probability (Math AI HL)

IB Mathematics: Applications and Interpretation • Unit 4

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Contents

  • Tree diagram structure
  • Two-stage and multi-stage trees
  • Conditional probability with trees
  • Using tree outcomes to find other probabilities

Tree diagram structure

How tree diagrams work: Branches show all outcomes.

Label each branch with probability.

Path probability: multiply along branches.

Multiply along the branches and add the end-paths; for conditional probability restrict to one branch first.

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Worked example

Bag: 3 red, 2 blue.

Draw 2 (no replace).

Show tree and find P(2 red).

Solution

  1. Branch 1: First red (3/5)
  2. Sub-branch: Second red given first red (2/4)
  3. Path prob: (3/5)×(2/4)=6/20=3/10
  4. Branch 2: First blue (2/5) leads to second outcomes

Final answer

P(RR)=3/10. Multiply along red path.

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Multi-stage experiments

Stages: Each branch level represents one stage.

Second stage branches depend on first outcome.

Worked example

Spinner spun twice: P(red)=0.4, P(blue)=0.6.

Find all outcomes and probabilities.

Solution

  1. RR: 0.4×0.4=0.16
  2. RB: 0.4×0.6=0.24
  3. BR: 0.6×0.4=0.24
  4. BB: 0.6×0.6=0.36
  5. Total: 0.16+0.24+0.24+0.36=1 ✓

Final answer

All paths shown, sum=1.

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Conditional probability from trees

Reading conditional prob: P(B|A) is the probability on second-stage branch GIVEN first stage was A.

Worked example

Factory: Machine A makes 60% of items, 2% defective.

Machine B makes 40%, 3% defective.

Find P(defective|Machine A).

Solution

  1. Tree: First stage: A (0.6) or B (0.4)
  2. From A: defective (0.02) or good (0.98)
  3. P(defective|A)=0.02 (second-level branch)

Final answer

0.02. Branch probability in conditional state.

IB-style question — reverse conditional [6 marks]

A shop is supplied bread by two bakeries. Bakery X supplies 70% of the loaves and Bakery Y supplies the other 30%. 4% of Bakery X's loaves are stale, while 10% of Bakery Y's loaves are stale. A loaf is chosen at random.

(a) Find the probability that the loaf is stale.

(b) Given that the chosen loaf is stale, find the probability that it came from Bakery Y.

Step by step

  1. (a) A loaf is stale along two tree paths: from X, or from Y. Find each path then add.
  2. Substitute the branch probabilities.
  3. (b) Use the conditional rule. The intersection 'Y and stale' is the Bakery-Y path from part (a).
  4. Evaluate.

Final answer

(a) P(stale) = 0.058. (b) P(Y | stale) = 0.030/0.058 = 0.517 (3 s.f.).

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Combining outcomes

Worked example

From defective example: Find P(defective).

Solution

  1. P(defective)=P(D|A)×P(A)+P(D|B)×P(B)
  2. =(0.02)×(0.6)+(0.03)×(0.4)
  3. =0.012+0.012=0.024

Final answer

P(defective)=0.024. Add paths leading to defective.

IB Exam Questions on Tree Diagrams and Conditional Probability

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How Tree Diagrams and Conditional Probability Appears in IB Exams

Examiners use specific command terms when asking about this topic. Here's what to expect:

Define

Give the precise meaning of key terms related to Tree Diagrams and Conditional Probability.

AO1
Describe

Give a detailed account of processes or features in Tree Diagrams and Conditional Probability.

AO2
Explain

Give reasons WHY — cause and effect within Tree Diagrams and Conditional Probability.

AO3
Evaluate

Weigh strengths AND limitations of approaches in Tree Diagrams and Conditional Probability.

AO3
Discuss

Present arguments FOR and AGAINST with a balanced conclusion.

AO3

See the full IB Command Terms guide →

Related Math AI HL Topics

Continue learning with these related topics from the same unit:

4.1.1Population and Samples
4.1.2Data Classification
4.1.3Sampling Techniques
4.1.4Data Reliability and Outliers
View all Math AI HL topics

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