Resources For Teachers For Tutors For Students & Parents AI New Pricing
Year 10 Maths - 10 Probability

Conditional Probability

20 practice questions 1 video lesson Theory + worked examples
Practice 20 questions
Practice questions

Every question with a fully worked solution.

Start practising
Watch 1 video(s)
  • Conditional Probability - Video - Conditional Probability Watch
Create a free accountTrack your progress and save your work as you go.
Create free account

Theory

A conditional probability \(P(A\mid B)\) is the probability of \(A\) when you already know \(B\) has happened: \(P(A\mid B)=\dfrac{P(A\cap B)}{P(B)}\). This Year 10 maths page covers the language of "given", "if … then", "of" and "knowing that", the restricted sample space, the formula, conditional probabilities from Venn diagrams, tables and tree diagrams, reversing a tree, common mistakes, and testing independence.

\(P(A\mid B)\) is read "the probability of \(A\) given \(B\)". The condition \(B\) is information that has already been learned, so outcomes outside \(B\) are no longer possible. The sample space shrinks to \(B\).

With equally likely outcomes, \(P(A\mid B)=\dfrac{n(A\cap B)}{n(B)}\): count the outcomes in both, and divide by the number in the condition, not by the original total.

The order matters. \(P(A\mid B)\) and \(P(B\mid A)\) have the same numerator but different denominators, so they are usually different. A test that is positive for \(80\%\) of people with an illness does not mean \(80\%\) of positive people have it.

Events are independent when the condition makes no difference: \(P(A\mid B)=P(A)\), which is the same as \(P(A\cap B)=P(A)\times P(B)\). Mutually exclusive events with non-zero probabilities are never independent, because \(P(A\mid B)=0\).

A condition shrinks the sample spaceTwelve cards numbered 1 to 12. The cards that are not multiples of 3 are crossed out in grey. The multiples of 3, which are 3, 6, 9 and 12, are outlined in navy, and 6 and 12, which are also even, are red. Given a multiple of 3, 4 cards are left and 2 are even, so the probability is one half. 1 2 3 4 5 6 7 8 9 10 11 12 given: a multiple of 3 → 4 cards left even among them: 6 and 12 P(even | multiple of 3) = 2/4 = ½
"Given that" removes every outcome outside the condition. Count the event only among the outcomes that are left.
Tree diagram with conditional probabilitiesA tree. First branches: F with probability 0.3 and F complement with 0.7. From F: positive 0.8, negative 0.2. From F complement: positive 0.15, negative 0.85. The two positive paths are red: 0.3 times 0.8 equals 0.24, and 0.7 times 0.15 equals 0.105. The other paths are 0.06 and 0.595. 0.3F0.8+0.3 × 0.8 = 0.240.2−0.060.7F′0.15+0.7 × 0.15 = 0.1050.85−0.595
Second-stage branches are conditional probabilities, such as \(P(+\mid F)=0.8\). Multiplying along a path gives \(P(F\cap +)\) (Example 3).
\[P(A\mid B)=\dfrac{P(A\cap B)}{P(B)}\]
\[P(A\cap B)=P(B)\times P(A\mid B)\]
\[P(A'\mid B)=1-P(A\mid B)\]
P(A∩B)=P(B)×P(A∣B)
Given information as\(P(A\mid B)\) is
a list of equally likely outcomescount \(A\) among the outcomes in \(B\)
a Venn diagramoverlap \(\div\) whole circle \(B\)
a two-way tablecell \(\div\) row or column total for \(B\)
a tree diagram (forwards)read the second-stage branch after \(B\)
a tree diagram (backwards)the path with both \(\div\) the sum of all paths ending in \(B\)
"At least one" as a condition. If the condition is "at least one", find its probability with the complement, \(1-P(\text{none})\). If the event is inside the condition (for example "both" is inside "at least one"), then \(P(\text{both}\cap\text{at least one})=P(\text{both})\), so divide \(P(\text{both})\) by \(P(\text{at least one})\).
Without replacement. Drawing a second item is a conditional probability: \(P(\text{2nd red}\mid\text{1st red})\) uses the bag as it is after the first draw, with one fewer red and one fewer item.
The language of conditions. "Given that", "if … then …", "knowing that", "of" (as in "of the students who walk, …") and "a student who …" all name the condition, which becomes the new total. "\(18\) of the \(60\) walkers are in Year 7" gives \(P(\text{Year 7} \mid \text{walks})=\dfrac{18}{60}=\dfrac{3}{10}\), which is not the same as \(P(\text{walks} \mid \text{Year 7})\).

How to find a conditional probability

  1. Identify the condition: the part after "given that", "if", or the item already known ("a student who…").
  2. Restrict the sample space to the condition. Find its size or probability.
  3. Count the event inside it: the outcomes in both the event and the condition.
  4. Divide: both \(\div\) condition. Simplify.

Other question types

  1. Find \(P(A\cap B)\): multiply \(P(A)\) by \(P(B\mid A)\). For \(A\) and not \(B\), multiply by \(1-P(B\mid A)\).
  2. Reverse a tree: add every path that ends in the given outcome to get the denominator, then divide the path you want by it (Example 3).
  3. Test independence: compare \(P(A\mid B)\) with \(P(A)\), or \(P(A\cap B)\) with \(P(A)P(B)\).
  4. Unknown counts: write the conditional probability with a pronumeral, such as \(\dfrac{n-1}{n+3}\), and solve (Example 4).
  5. Two items at once: a die and a coin, or two dice, can be listed as ordered pairs. Cross out the pairs outside the condition, then count.
Example 1 — Reduce the sample space

(i) A card is chosen from cards numbered \(1\) to \(30\). Given that the number is odd, find the probability that it is prime.
(ii) Two dice are rolled. Given that the sum is \(7\), find the probability that one of the dice shows a \(2\).
Solution

(i) The condition leaves the \(15\) odd numbers. The odd primes up to \(30\) are \(3, 5, 7, 11, 13, 17, 19, 23, 29\): \(9\) of them (\(1\) is not prime).

\(P(\text{prime}\mid\text{odd})\)\(=\)\(\dfrac{9}{15}=\boldsymbol{\dfrac{3}{5}}\)

(ii) A sum of \(7\) leaves \(6\) outcomes: \((1,6), (2,5), (3,4), (4,3), (5,2), (6,1)\). Two of them contain a \(2\).

\(P(\text{a } 2\mid\text{sum } 7)\)\(=\)\(\dfrac{2}{6}=\boldsymbol{\dfrac{1}{3}}\)

Without the condition, \(P(\text{a } 2 \text{ on either die})=\dfrac{11}{36}\). The condition changes the answer.

Example 2 — Using the formula
\(P(A)=0.5\), \(P(B)=0.25\) and \(P(A\cap B)=0.09\).
(i) Find \(P(A\mid B)\) and \(P(B\mid A)\).
(ii) Are \(A\) and \(B\) independent?
Solution

(i) Divide the probability of both by the probability of the condition.

\(P(A\mid B)\)\(=\)\(\dfrac{P(A\cap B)}{P(B)}=\dfrac{0.09}{0.25}=\boldsymbol{0.36}\)
\(P(B\mid A)\)\(=\)\(\dfrac{P(A\cap B)}{P(A)}=\dfrac{0.09}{0.5}=\boldsymbol{0.18}\)
P(A∣B)=P(A∩B)P(B)

(ii) \(P(A\mid B)=0.36\) but \(P(A)=0.5\). Knowing \(B\) happened changes the chance of \(A\), so they are not independent. Check: \(P(A)\times P(B)=0.125\neq 0.09\).

Example 3 — Reversing a tree
\(30\%\) of patients at a clinic have the flu (\(F\)). A rapid test is positive for \(80\%\) of patients with the flu and \(15\%\) of patients without it.
(i) Find \(P(+)\).
(ii) A patient tests positive. Find the probability that they have the flu, correct to \(3\) decimal places.
Solution

(i) Two paths in the tree end in a positive result. Add them.

\(P(+)\)\(=\)\(0.3\times 0.8+0.7\times 0.15\)
\(\)\(=\)\(0.24+0.105=\boldsymbol{0.345}\)

(ii) The condition is a positive test, so divide the flu-and-positive path by \(P(+)\).

\(P(F\mid +)\)\(=\)\(\dfrac{0.24}{0.345}\)
\(\)\(\approx\)\(\boldsymbol{0.696}\)

Notice \(P(F\mid +)\approx 0.696\) is not the same as \(P(+\mid F)=0.8\).

Example 4 — An unknown in the bag
A box holds \(n\) green balls and \(4\) yellow balls. Two balls are taken out one after the other, without replacement. Given that the first ball is green, the probability that the second is green is \(\dfrac{1}{2}\).
(i) Find \(n\).
(ii) Find the probability that both balls are green.
Solution

(i) After one green ball is removed, \(n-1\) green and \(4\) yellow are left, \(n+3\) balls in total.

\(\dfrac{n-1}{n+3}\)\(=\)\(\dfrac{1}{2}\)
\(2(n-1)\)\(=\)\(n+3\)
\(n\)\(=\)\(\boldsymbol{5}\)

(ii) The box starts with \(5\) green and \(4\) yellow, \(9\) balls.

\(P(GG)\)\(=\)\(P(G_1)\times P(G_2\mid G_1)\)
\(\)\(=\)\(\dfrac{5}{9}\times\dfrac{4}{8}=\boldsymbol{\dfrac{5}{18}}\)

Common pitfalls

Divide by the condition, not the whole. Given the sum is \(7\), there are \(6\) possible outcomes, not \(36\).
\(P(A\mid B)\neq P(B\mid A)\). In Example 3, \(P(+\mid F)=0.8\) but \(P(F\mid +)\approx 0.696\). Read which event is the condition.
Divide by P(B), do not multiply. \(P(A\mid B)=\dfrac{0.09}{0.25}\), not \(0.09\times 0.25\).
Update the bag. After drawing a red counter without replacement, both the number of reds and the total drop by \(1\).
Mutually exclusive is not independent. If \(A\) and \(B\) cannot both happen, then knowing \(B\) tells you \(A\) did not happen, which is the opposite of independence.

Frequently asked questions

What is conditional probability?

It is the probability of an event when you already know that another event has happened. Written \(P(A\mid B)\), it uses only the outcomes in \(B\) as the sample space.

What is the formula for conditional probability?

\(P(A\mid B)=\dfrac{P(A\cap B)}{P(B)}\). With equally likely outcomes this is the number of outcomes in both, divided by the number in \(B\).

How do you find a conditional probability from a tree diagram?

The second-stage branches already show conditional probabilities. To go backwards, add the probabilities of all paths that end in the given outcome, then divide the path you want by that sum.

How do you know if two events are independent?

\(A\) and \(B\) are independent if \(P(A\mid B)=P(A)\), or equivalently if \(P(A\cap B)=P(A)\times P(B)\). Knowing one happened does not change the chance of the other.

Why is P(A given B) different from P(B given A)?

Both use the outcomes in \(A\) and \(B\) for the numerator, but one divides by the number in \(B\) and the other by the number in \(A\). Unless \(A\) and \(B\) are the same size, the answers differ.