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Probability rules and combinations (VCE Maths Methods Unit 1)

Syllabus dot point

“Apply the rules of probability (addition, multiplication, conditional), the counting principles (permutations and combinations), and use these to find probabilities in compound experiments”

7 min read

Quick answer

Use the addition rule P(A∪B)=P(A)+P(B)−P(A∩B)P(A \cup B) = P(A) + P(B) - P(A \cap B), the multiplication rule P(A∩B)=P(A)P(B∣A)P(A \cap B) = P(A)P(B|A) and conditional probability P(B∣A)=P(A∩B)/P(A)P(B|A) = P(A \cap B)/P(A). Count with permutations nPr^nP_r when order matters and combinations nCr^nC_r when it does not.

Jump to a section
  1. What this dot point is asking
  2. Probability rules
  3. Counting
  4. When to use which
  5. Independence versus mutual exclusivity
  6. Tree diagrams and Karnaugh tables
  7. In one sentence
  8. Examples in context
  9. Try this
  10. Exam-style questions

What this dot point is asking

VCAA wants you to apply the rules of probability and the counting principles to compute probabilities of compound events, distinguishing situations where order matters (permutations) from those where it does not (combinations).

Probability rules

Probability of an event AA
P(A)=P(A) = favourable outcomes / total outcomes (for equally likely outcomes).
Complement
P(Ac)=1−P(A)P(A^c) = 1 - P(A).
Addition rule (union of events)
P(A∪B)=P(A)+P(B)−P(A∩B)P(A \cup B) = P(A) + P(B) - P(A \cap B). For mutually exclusive events, P(A∩B)=0P(A \cap B) = 0.
Multiplication rule (intersection)
P(A∩B)=P(A)⋅P(B∣A)P(A \cap B) = P(A) \cdot P(B|A). For independent events, P(B∣A)=P(B)P(B|A) = P(B), so P(A∩B)=P(A)P(B)P(A \cap B) = P(A) P(B).
Conditional probability
P(B∣A)=P(A∩B)/P(A)P(B|A) = P(A \cap B)/P(A), the probability of BB given AA has occurred.

Counting

Multiplication principle. If task 11 has n1n_1 ways and task 22 has n2n_2 ways, then the combined task has n1⋅n2n_1 \cdot n_2 ways.

Permutations (order matters). Arrangements of rr items from nn:

nPr=n!(n−r)!^nP_r = \frac{n!}{(n-r)!}

Combinations (order doesn't matter). Selections of rr from nn:

nCr=(nr)=n!r!(n−r)!^nC_r = \binom{n}{r} = \frac{n!}{r!(n-r)!}

When to use which

Scenario Counting tool
Arrange rr items in order nPr^nP_r
Choose rr items, order doesn't matter nCr^nC_r
Choose with replacement nrn^r
Distinct seating nPr^nP_r or n!n!
Distinct committees nCr^nC_r

Independence versus mutual exclusivity

Two terms are routinely confused. Events are mutually exclusive when they cannot both occur, so P(A∩B)=0P(A \cap B) = 0 and the addition rule simplifies to P(A∪B)=P(A)+P(B)P(A \cup B) = P(A) + P(B). Events are independent when one occurring does not change the probability of the other, so P(A∩B)=P(A)P(B)P(A \cap B) = P(A)P(B), equivalently P(B∣A)=P(B)P(B \mid A) = P(B). These are different (indeed almost opposite) ideas: two events with non-zero probabilities cannot be both mutually exclusive and independent, because mutual exclusivity forces P(A∩B)=0P(A \cap B) = 0 while independence forces P(A∩B)=P(A)P(B)>0P(A \cap B) = P(A)P(B) > 0. VCAA frequently asks you to test independence by comparing P(A∩B)P(A \cap B) with P(A)P(B)P(A)P(B).

Tree diagrams and Karnaugh tables

For multi-stage experiments, a tree diagram multiplies along branches (the multiplication rule) and adds across distinct paths (the addition rule). A two-by-two probability table (Karnaugh table) is the fastest tool for problems framed with "and", "or" and "given that", because the margins give P(A)P(A) and P(B)P(B) while the interior cells give the intersections. Filling the table from the information supplied, then reading off the required cell or margin, avoids most conditional-probability slips.

Drawing without replacement

Step 1: Identify the setup and what changes after each draw

A bag contains 44 red and 66 blue marbles, giving 1010 marbles in total. Two marbles are drawn without replacement, meaning once the first marble is removed the total count drops and the composition of the bag changes for the second draw. We want P(both red)P(\text{both red}).

Step 2: Apply the multiplication rule for dependent events

Because the draws are dependent (the outcome of the first affects the second), we use P(A∩B)=P(A)×P(B∣A)P(A \cap B) = P(A) \times P(B \mid A). The first draw is red with probability 410\dfrac{4}{10}, and given that first marble was red, there are now 33 reds remaining out of only 99 marbles.

P(both red)=410⋅39=1290=215P(\text{both red}) = \dfrac{4}{10} \cdot \dfrac{3}{9} = \dfrac{12}{90} = \dfrac{2}{15}

Step 3: Confirm using combinations

As a check, we can count directly: the number of ways to choose 22 reds from 44 divided by the number of ways to choose any 22 from 1010 gives the same probability.

4C210C2=645=215✓\dfrac{{}^4C_2}{{}^{10}C_2} = \dfrac{6}{45} = \dfrac{2}{15} \checkmark

Final answer: P(both red)=215P(\text{both red}) = \dfrac{2}{15}.

Common traps
Using nPr^nP_r instead of nCr^nC_r
For unordered selections (committees, hands of cards), use combinations.
Forgetting "without replacement" reduces the population
After one draw, the second draw is from 99 marbles, not 1010.
Treating dependent events as independent
Use conditional probability when one event changes the other's likelihood.
Forgetting the intersection in the addition rule
P(A)+P(B)P(A) + P(B) double-counts P(A∩B)P(A \cap B), so subtract it.

In one sentence

Probability is the ratio of favourable to total outcomes, governed by the addition rule (P(A∪B)=P(A)+P(B)−P(A∩B)P(A \cup B) = P(A) + P(B) - P(A \cap B)), the multiplication rule (P(A∩B)=P(A)P(B∣A)P(A \cap B) = P(A) P(B|A)) and conditional probability (P(B∣A)=P(A∩B)/P(A)P(B|A) = P(A \cap B)/P(A)); counting uses the multiplication principle, permutations (nPr^nP_r when order matters) and combinations (nCr^nC_r when it does not).

Examples in context

Example 1. Selecting a sports squad. A coach picks a netball lineup of 77 from a squad of 1010. Order on the team sheet does not matter, so the number of possible lineups is 10C7=10!7! 3!=120^{10}C_7 = \dfrac{10!}{7!\,3!} = 120. If exactly 33 specific players must be included, the remaining 44 are chosen from the other 77: 7C4=35^7C_4 = 35 lineups.

Example 2. Lottery-style probability. A simple draw selects 33 numbered balls from 2020 without replacement. The probability of matching a chosen set of 33 (order irrelevant) is 120C3=11140≈0.00088\dfrac{1}{^{20}C_3} = \dfrac{1}{1140} \approx 0.00088. Using the multiplication rule directly: 320×219×118=66840=11140\dfrac{3}{20} \times \dfrac{2}{19} \times \dfrac{1}{18} = \dfrac{6}{6840} = \dfrac{1}{1140}, confirming the same result.

Try this

Q1. How many ways can 55 books be arranged on a shelf? [1 mark]

  • Cue. 5!=1205! = 120.

Q2. A committee of 33 is chosen from 55 teachers and 44 students. Find the probability it contains exactly 11 teacher. [3 marks]

  • Cue. 5C1⋅4C29C3=5×684=3084=514\dfrac{^5C_1 \cdot {}^4C_2}{^9C_3} = \dfrac{5 \times 6}{84} = \dfrac{30}{84} = \dfrac{5}{14}.

Q3. From 88 red and 22 white balls, 22 are drawn without replacement. Find P(at least one white)P(\text{at least one white}). [3 marks]

  • Cue. 1−P(no white)=1−810⋅79=1−5690=3490=17451 - P(\text{no white}) = 1 - \dfrac{8}{10}\cdot\dfrac{7}{9} = 1 - \dfrac{56}{90} = \dfrac{34}{90} = \dfrac{17}{45}.

Exam-style questions

Questions in the style of VCAA exam questions on this dot point, each with a worked answer. They are written by ExamExplained unless tagged "Past paper"; the year shows the paper a question is modelled on.

Practice4 marks
A committee of 44 is chosen from 66 men and 55 women. (a) How many possible committees are there? (b) What is the probability the committee has exactly 22 men and 22 women?
Show worked answer →

(a) Total committees. 11C4=11!4!⋅7!=330^{11}C_4 = \dfrac{11!}{4! \cdot 7!} = 330.

(b) Exactly 22 men and 22 women. Choose 22 from 66 men and 22 from 55 women.

6C2⋅5C2=15⋅10=150^6C_2 \cdot {}^5C_2 = 15 \cdot 10 = 150.

Probability: 150/330=5/11≈0.455150/330 = 5/11 \approx 0.455.

Markers reward correct use of combinations (order does not matter), multiplication of the two choices, and probability as a ratio.

Practice5 marks
Events AA and BB satisfy P(A)=0.5P(A) = 0.5, P(B)=0.4P(B) = 0.4 and P(A∪B)=0.7P(A \cup B) = 0.7. (a) Calculate P(A∩B)P(A \cap B). (b) Determine P(B∣A)P(B \mid A). (c) State, with justification, whether AA and BB are independent.
Show worked answer →

(a) By the addition rule, P(A∩B)=P(A)+P(B)−P(A∪B)=0.5+0.4−0.7=0.2P(A \cap B) = P(A) + P(B) - P(A \cup B) = 0.5 + 0.4 - 0.7 = 0.2.

(b) P(B∣A)=P(A∩B)P(A)=0.20.5=0.4P(B \mid A) = \dfrac{P(A \cap B)}{P(A)} = \dfrac{0.2}{0.5} = 0.4.

(c) AA and BB are independent if P(A∩B)=P(A)P(B)P(A \cap B) = P(A)P(B). Here P(A)P(B)=0.5×0.4=0.2P(A)P(B) = 0.5 \times 0.4 = 0.2, which equals P(A∩B)=0.2P(A \cap B) = 0.2, so the events are independent. (Equivalently, P(B∣A)=0.4=P(B)P(B \mid A) = 0.4 = P(B).)

Markers reward the addition rule rearrangement, the conditional-probability formula, and a correct independence test with the comparison stated.

Practise this

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