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Basic ProbabilityEdexcel GCSE Maths: Revision notes

Section 1

The Probability Scale

Probability measures how likely an event is to happen, on a scale from 0 (impossible) to 1 (certain). It can be written as a fraction, decimal or percentage.

ProbabilityLanguage
0Impossible
Close to 0Unlikely
0.5Even chance
Close to 1Likely
1Certain

Probabilities can never be negative or greater than 1.

Key termsprobability
Common mistake

A calculated probability of, for example, 1.2 or −0.3 is always wrong — probabilities must lie between 0 and 1 inclusive.

Section 2

Equally Likely Outcomes and Expected Number

When all outcomes of an experiment are equally likely (each just as likely as any other), the probability of a specific event is:

P(event) = number of favourable outcomes ÷ total number of possible outcomes

This assumes randomness and fairness — for example, a fair coin or an unbiased die. From a probability, you can calculate the expected number of successes in n trials: expected number = probability × n.

Example: rolling a fair die, P(rolling a 6) = 1/6. In 60 rolls, the expected number of 6s = (1/6) × 60 = 10.

Key termsequally likely outcomesexpected number

Section 3

Relative Frequency vs Theoretical Probability

Theoretical probability is calculated using known facts about equally likely outcomes (e.g. 1/6 for a die). Relative frequency (experimental probability) is calculated from actual results of an experiment:

relative frequency = number of times an event occurs ÷ total number of trials

Relative frequency is only an estimate of the true probability, but the more trials carried out, the closer the relative frequency tends to get to the theoretical probability — larger samples give better estimates.

Key termstheoretical probabilityrelative frequency
Example

A coin is flipped 100 times and lands heads 47 times. Relative frequency of heads = 47/100 = 0.47, close to the theoretical probability of 0.5.

Section 4

Probabilities Sum to 1

For any exhaustive set of outcomes (an outcome from the set must happen), the probabilities of all the outcomes add up to exactly 1.

This is especially useful for mutually exclusive events (events that cannot happen at the same time) — if you know the probability of an event happening, the probability of it NOT happening is 1 minus that probability.

Example: P(rain tomorrow) = 0.3, so P(no rain tomorrow) = 1 − 0.3 = 0.7.

Key termsexhaustive eventsmutually exclusive events

Section 5

Two-Way Tables

A two-way table organises data or outcomes by two categories at once (rows and columns), making it easy to read off probabilities directly.

To find a probability from a two-way table:

  1. Identify the correct row/column total for the event described
  2. Divide the relevant frequency by the appropriate total (the grand total, or a row/column total for conditional probabilities)

Two-way tables are a clear, systematic way to enumerate outcomes without missing any combinations.

Key termstwo-way table

Must Know

  • Probability lies between 0 (impossible) and 1 (certain), inclusive
  • For equally likely outcomes: P(event) = favourable outcomes ÷ total outcomes
  • Expected number of successes in n trials = probability × n
  • Relative frequency = number of successes ÷ number of trials; it estimates probability, improving with more trials
  • Probabilities of an exhaustive set of outcomes sum to 1; for mutually exclusive events, P(not A) = 1 − P(A)
  • Two-way tables organise outcomes by two categories, making probabilities easy to read off

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