Probability is all about how likely something is to happen. We use it every day, often without realising it:
In mathematics, probability gives us a precise way to talk about that “chance”. In this article you’ll learn key ideas and language used in Year 10 probability: sample space, trials, simple events, compound events, and how to use Venn diagrams.
Work through the sections in order, then try the linked Year 10 skill quizzes after each section.
A random experiment is any situation where:
A single performance of that experiment is called a trial.
Example:
The sample space is the set of all possible outcomes of an experiment.
Example: Rolling a fair six-sided die
Outcomes: 1, 2, 3, 4, 5, 6
Sample space:
An event is a collection of outcomes that we’re interested in. Events are usually written as sets.
Example: Event “rolling an even number”
Outcomes that are even: 2, 4, 6
Event:
For simple situations where all outcomes are equally likely, we use:
Probability = Number of favourable outcomes ÷ Total number of outcomes
Example: Probability of rolling a 3 on a fair die
So, .
Probabilities are numbers between 0 and 1 (or 0% and 100%):
Practise distinguishing the full set of outcomes from the event of interest.
A simple event is an event that involves just a single step or a single outcome type. There is only one “thing” happening.
Examples of simple events:
For simple events with equally likely outcomes, we still use:
Pr(event) = Number of favourable outcomes ÷ Total number of outcomes
Example: Drawing a red ball
A bag contains 3 red balls and 5 blue balls. One ball is drawn at random.
.
Count the favourable outcomes and total equally likely outcomes before forming the probability fraction.
A compound event involves more than one step or more than one condition. For example:
When there are two steps, you can think in terms of ordered pairs, like: (outcome of first step, outcome of second step).
Example: Flipping a coin twice
Each flip can be Heads (H) or Tails (T).
Sample space:
So, .
When dealing with compound events, you’ll often see the words AND and OR:
Example: Rolling two dice
Two events are independent if one does not affect the probability of the other.
Example: Flip a coin and roll a die
What happens on the coin doesn’t change what happens on the die, so A and B are independent.
For independent events A and B, the probability of both happening (A AND B) is:
Pr(A and B) = Pr(A) × Pr(B)
Example: Heads and a 6
So, .
List combined outcomes, then practise multiplying probabilities when the events are independent.
Venn diagrams are used to visually show how events overlap. They are especially useful for “A AND B”, “A OR B” and “not A” (the complement of A).
A Venn diagram usually looks like this:
Example: Students in a Year 10 class
Let’s say:
Some students might be in both circles (they play both sports). Some may be outside both circles (they play neither).
The union of A and B (written as ) includes all outcomes that are in A, or in B, or in both.
On a Venn diagram, this is the area covered by either circle.
Probability formula for union (general form):
We subtract (the intersection) because it’s counted twice in .
The intersection of A and B (written as ) includes only outcomes that are in both A and B.
On the Venn diagram, this is where the two circles overlap.
Example: Students who play both sports
If 12 students play soccer, 10 students play basketball and 5 students play both, then:
The number who play soccer or basketball (or both) is:
.
The complement of A (written as or ) means “not A” – all outcomes that are outside event A.
On the Venn diagram, this is everything in the rectangle that is not in circle A.
We have the useful relationship:
Pr(not A) = 1 − Pr(A)
Example: Complement
If the probability that a student catches the bus to school is 0.4, then:
.
Use the two circles to identify overlaps and outside regions, then connect the diagram with a two-way table.
Let’s summarise the key terms:
Understanding these ideas sets you up for more advanced probability topics in Year 10 and beyond, including tree diagrams, conditional probability and real-life applications in science, finance, and everyday decision-making.
Review sample spaces and simple probabilities before tackling compound events.