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Probability and counting: VCE Math Methods Unit 1 Year 11

Syllabus dot point

“Counting principles (multiplication principle, permutations and combinations), set notation, simple probability, conditional probability and the addition / multiplication rules”

8 min read

Quick answer

Unit 1 probability combines counting principles (multiplication rule, permutations, combinations), set notation for events, simple probability (P=∣A∣/∣S∣P = |A|/|S| for equally likely outcomes), 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)), conditional probability (P(A∣B)=P(A∩B)/P(B)P(A|B) = P(A \cap B)/P(B)), and the multiplication rule, with independence (P(A∩B)=P(A)P(B)P(A \cap B) = P(A) P(B)) as a special case that simplifies many calculations.

Jump to a section
  1. What this dot point is asking
  2. Counting principles
  3. Set notation for events
  4. Simple probability
  5. The addition rule
  6. Conditional probability
  7. The multiplication rule
  8. Worked example: conditional probability
  9. Worked example: tree diagrams
  10. Examples in context
  11. Try this
  12. Exam-style questions

What this dot point is asking

VCAA wants you to apply counting principles (multiplication, permutations, combinations) to count outcomes in probability problems, use set notation to describe events, and compute simple probabilities, conditional probabilities and combined probabilities via the addition and multiplication rules.

Counting principles

The multiplication principle

If an event can occur in mm ways followed by another in nn ways, the combined event can occur in m×nm \times n ways.

Example. A menu has 4 mains and 3 desserts. The number of main-plus-dessert combinations is 4×3=124 \times 3 = 12.

Permutations

A permutation is an arrangement of items in order. The number of ways to arrange nn distinct items in order is:

n!=n×(n−1)×(n−2)×…×2×1n! = n \times (n-1) \times (n-2) \times \ldots \times 2 \times 1

The number of ways to choose and arrange kk items from nn (order matters) is:

P(n,k)=n!(n−k)!P(n, k) = \frac{n!}{(n-k)!}

Example. Number of ways to award gold, silver, bronze from 8 finalists: P(8,3)=8!/5!=8⋅7⋅6=336P(8, 3) = 8! / 5! = 8 \cdot 7 \cdot 6 = 336.

Combinations

A combination is a selection without regard to order. The number of ways to choose kk items from nn (order does not matter) is:

(nk)=n!k!(n−k)!\binom{n}{k} = \frac{n!}{k! (n-k)!}

Example. Number of ways to choose 5 students from 30: (305)=142,506\binom{30}{5} = 142,506.

Set notation for events

In probability, an event is a set of outcomes.

  • Sample space SS: the set of all possible outcomes.
  • Event AA: a subset of SS.
  • Union A∪BA \cup B: outcomes in AA or BB (or both).
  • Intersection A∩BA \cap B: outcomes in both AA and BB.
  • Complement A′A' or Aˉ\bar{A}: outcomes not in AA.

Simple probability

For a sample space with equally likely outcomes:

P(A)=∣A∣∣S∣=number of outcomes in Atotal number of outcomesP(A) = \frac{|A|}{|S|} = \frac{\text{number of outcomes in } A}{\text{total number of outcomes}}

Properties:

  • 0≤P(A)≤10 \leq P(A) \leq 1.
  • P(S)=1,P(∅)=0P(S) = 1, P(\emptyset) = 0.
  • P(A′)=1−P(A)P(A') = 1 - P(A).

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)

For mutually exclusive events (no overlap), P(A∩B)=0P(A \cap B) = 0, so P(A∪B)=P(A)+P(B)P(A \cup B) = P(A) + P(B).

Conditional probability

The probability of AA given that BB has occurred:

P(A∣B)=P(A∩B)P(B)P(A | B) = \frac{P(A \cap B)}{P(B)}

(provided P(B)>0P(B) > 0).

Interpretation: conditional probability restricts attention to outcomes where BB has occurred.

The multiplication rule

P(A∩B)=P(A∣B)⋅P(B)=P(B∣A)⋅P(A)P(A \cap B) = P(A | B) \cdot P(B) = P(B | A) \cdot P(A)

For independent events, P(A∣B)=P(A)P(A | B) = P(A), so P(A∩B)=P(A)⋅P(B)P(A \cap B) = P(A) \cdot P(B).

Independence test

Events AA and BB are independent if and only if P(A∩B)=P(A)⋅P(B)P(A \cap B) = P(A) \cdot P(B). Equivalently, P(A∣B)=P(A)P(A | B) = P(A).

Worked example: conditional probability

In a class of 30 students, 18 study Maths, 12 study Physics, and 8 study both.

P(Maths)=18/30=0.6P(\text{Maths}) = 18/30 = 0.6.

P(Physics)=12/30=0.4P(\text{Physics}) = 12/30 = 0.4.

P(Both)=8/30≈0.267P(\text{Both}) = 8/30 \approx 0.267.

P(Physics∣Maths)=P(Both)/P(Maths)=(8/30)/(18/30)=8/18≈0.444P(\text{Physics} | \text{Maths}) = P(\text{Both}) / P(\text{Maths}) = (8/30) / (18/30) = 8/18 \approx 0.444.

Among Maths students, the conditional probability of also doing Physics is about 0.444.

Worked example: tree diagrams

A box contains 5 red and 3 blue balls. Two are drawn without replacement.

P(both red)=P(first red)⋅P(second red∣first red)=(5/8)⋅(4/7)=20/56=5/14P(\text{both red}) = P(\text{first red}) \cdot P(\text{second red} | \text{first red}) = (5/8) \cdot (4/7) = 20/56 = 5/14.

P(first red, second blue)=(5/8)⋅(3/7)=15/56P(\text{first red, second blue}) = (5/8) \cdot (3/7) = 15/56.

Tree diagrams help visualise: at each node, the conditional probability depends on what has been drawn already.

Common errors
Confusing permutations and combinations
Permutations: order matters. Combinations: order does not.
Forgetting the overlap
P(A∪B)=P(A)+P(B)−P(A∩B)P(A \cup B) = P(A) + P(B) - P(A \cap B). Forgetting the subtraction double-counts.
Conditional probability backwards
P(A∣B)P(A | B) is generally not equal to P(B∣A)P(B | A). Bayes's theorem relates the two.
Treating dependent events as independent
Drawing without replacement: the second draw depends on the first. Always check whether sampling is with or without replacement.
Misreading the question
"At least one" usually means "1 - none". "Exactly one" is different from "at least one".

Examples in context

Example 1. Arranging a roster. A cafe must roster 33 of its 77 baristas to the three distinct shifts (morning, midday, evening) on a given day. Since each shift is a different role, order matters, so the count is a permutation: P(7,3)=7!4!=7×6×5=210P(7, 3) = \frac{7!}{4!} = 7 \times 6 \times 5 = 210 ways. If instead the manager just picks a team of 33 with no shift assignment, order does not matter, giving (73)=35\binom{7}{3} = 35 teams.

Example 2. Conditional probability in screening. Of 200200 surveyed shoppers, 8080 own a loyalty card and 5050 of those used a discount; 3030 non-card-holders used a discount. The probability a shopper used a discount given they hold a card is P(discount∣card)=5080=0.625P(\text{discount}\mid\text{card}) = \frac{50}{80} = 0.625. Compare P(discount)=80200=0.40P(\text{discount}) = \frac{80}{200} = 0.40: since 0.625≠0.400.625 \neq 0.40, using a discount is not independent of holding a card.

Try this

Q1. A PIN uses 44 digits from 00 to 99 with no repeats. How many are possible? [2 marks]

  • Cue. Permutation P(10,4)=10×9×8×7=5040P(10, 4) = 10 \times 9 \times 8 \times 7 = 5040.

Q2. Events AA and BB have P(A)=0.5P(A) = 0.5, P(B)=0.4P(B) = 0.4, P(A∩B)=0.2P(A \cap B) = 0.2. (a) Find P(A∪B)P(A \cup B). (b) Are AA and BB independent? [2+2 marks]

  • Cue. (a) 0.5+0.4−0.2=0.70.5 + 0.4 - 0.2 = 0.7. (b) P(A)P(B)=0.5×0.4=0.2=P(A∩B)P(A)P(B) = 0.5 \times 0.4 = 0.2 = P(A \cap B), so yes independent.

Q3. A bag has 44 red and 66 green discs. Two are drawn without replacement. Find the probability both are green. [3 marks]

  • Cue. 610×59=3090=13\frac{6}{10} \times \frac{5}{9} = \frac{30}{90} = \frac{1}{3}.

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.

Year 11 SAC-style4 marks
From a class of 30 students (18 male, 12 female), 5 are chosen at random. (a) How many ways can the 5 be chosen? (b) What is the probability that exactly 3 are female?
Show worked answer →

(a) Total ways. Choose 5 from 30: (305)=142,506\binom{30}{5} = 142,506.

(b) Probability exactly 3 female.

Ways to choose 3 female from 12: (123)=220\binom{12}{3} = 220.

Ways to choose 2 male from 18: (182)=153\binom{18}{2} = 153.

Total favourable: 220×153=33,660220 \times 153 = 33,660.

Probability: 33,660/142,506≈0.23633,660 / 142,506 \approx 0.236.

Markers reward the combination formulas, the multiplication principle to combine the gender selections, and a probability with sensible precision.

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