Edexcel · GCSE Maths · 1MA1 · Foundation and Higher
M36 · Probability and experiments
Revision notes, worked examples and methods for probability and experiments.
Notes and quizzes ready · 50 questions · Video coming soon.
Foundation shows shared notes and questions. Higher includes the shared content and labelled Higher extensions. Changing tier starts the notes and a new quiz set.
Revise the key ideas
Probability language and calculations
A probability lies from 0 to 1 inclusive. Impossible events have probability 0; certain events have probability 1. A probability of 0.5 describes an even chance. The probability scale
For equally likely outcomes, P(event) = number of favourable outcomestotal number of possible outcomes. Count outcomes rather than assuming every named category is equally likely.
Worked example: On a fair six-sided die, multiples of 3 are 3 and 6, so their probability is 26 = 13. Fairness means each face has equal probability.
The probabilities of an exhaustive set of mutually exclusive outcomes sum to 1. Exhaustive means all possibilities are covered; mutually exclusive means they cannot happen together.
For a complementary event, P(not A) = 1 − P(A). If rain has probability 0.3, no rain has probability 0.7 under that model.
For mutually exclusive events, P(A or B) = P(A) + P(B). If they overlap, simply adding double-counts the overlap; account for it using a Venn diagram.
Experiments and expected outcomes
Relative frequency = number of times the event occursnumber of trials. This estimates probability from observations; it is not necessarily the exact theoretical value.
Worked example: A spinner lands on red 38 times in 100 spins. Estimated P(red) = 0.38. If this model holds, 250 future spins would have about 250 × 0.38 = 95 red results.
Expected frequency = probability × number of trials. With P(success) = 14, 80 trials have expected frequency 20; the actual count can differ.
More trials usually make an unbiased relative-frequency estimate more reliable. It tends towards the theoretical probability for a suitable stable random model, but need not get closer after every extra trial.
Small samples can give misleading proportions. Use a sufficiently large, fair experiment and keep the conditions consistent when estimating future outcomes.
Independent trials do not “balance themselves out” on the next trial. After five heads on a fair coin, the next toss still has P(heads) = 12.
Organising evidence
Frequency tables record counts by outcome. A frequency tree splits a total into groups; each parent's count must equal the sum of its branches. Counts in a frequency tree
Worked example: Of 60 students, 35 walk and 25 use transport. If 20 walkers and 10 transport users bring lunch, the four leaf counts are 20, 15, 10 and 15. They total 60.
State assumptions such as a fair die, a representative sample or unchanged conditions. A mathematically correct calculation can still be an unreliable prediction if its model is inappropriate.
Test yourself
50 questions · Sets of 10 from the selected tier. For fractions, use / when typing; for powers, use superscripts or ^. Follow each question's answer format. These quick checks support revision; practise full written solutions and proofs too.
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