Listing outcomes, finding probabilities, and comparing them with relative frequency.
Practise Probability in the app →
What gets asked
- List all the possible outcomes of a simple situation.
- Write the probability of an outcome as a fraction.
- Predict the relative frequency of an outcome over many trials.
- Explain why relative frequency and probability are not always exactly equal.
You must be able to
- List every outcome once, without missing or repeating any.
- Put the number of favourable outcomes over the total number of outcomes.
- Scale a probability up to predict results over many trials.
- Explain a gap between relative frequency and probability using sample size.
Traps that cost marks
- Treating identical-looking items in a bag as a single outcome. Each item is a separate outcome, even if two look the same.
- Writing a probability as favourable outcomes over the outcomes that did not happen. A probability is always favourable outcomes over the total number of outcomes.
- Predicting that every outcome will happen exactly the same number of times. Scale each outcome by its own probability. A more likely outcome should happen more often.
- Deciding a coin or die is unfair as soon as one result differs from the expected probability. A small number of trials can differ from the probability just by chance. That does not mean it is unfair.
Worked example
- Total sweets .
- Blue sweets .
- .
Outcomes and relative frequency
Listing everything that can happen, saying how likely each one is, and checking that against a real try.
- outcome
- One of the things that can happen. Drawing a red sweet is one outcome.
- trial
- One go: one toss of a coin, or one draw from a bag.
- probability
- How likely an outcome is, written as a fraction from to .
- equally likely
- Every outcome has the same chance, as on a fair coin or a fair die.
- relative frequency
- How many times the outcome really happened, divided by the number of trials.
Start by listing every outcome, one by one. A bag holding red and blue sweets has outcomes, not . Two sweets of one colour are still two separate outcomes.
Probability counts ways. Put the number of ways your outcome can happen over the total number of outcomes. It runs from , meaning impossible, up to , meaning certain.
This year every situation has outcomes that are equally likely. A fair coin, a fair die and a well-mixed bag all behave that way, so counting the items is enough.
To predict, multiply the probability by the number of trials. A probability of over trials predicts about . You are predicting a number of draws, not a fraction.
The relative frequency is what really happened. It seldom lands exactly on the probability, and a small gap proves nothing about fairness. The more trials you do, the closer it usually comes.
Rules to remember
- P(outcome)
- predicted number probability number of trials
Examples
Worked answer
- Count every sweet: .
- Each sweet is one outcome, even two greens that look alike.
- Four of those twelve outcomes are blue.
- P(blue) .
Answer:
The total goes underneath. Blue over the other eight sweets would be wrong.
Worked answer
- The probability of blue is .
- Multiply it by the number of trials: .
- So about of the draws should be blue.
Answer: about blues
Red is less likely than blue, so the three colours will not come up equally often.
Worked answer
- Relative frequency of blue: .
- The probability was .
- You expected and got , so .
- A gap of in trials turns up easily by chance.
Answer: no, a gap that small is ordinary
Doing draws instead would usually bring the relative frequency nearer.
Traps
- Treating identical-looking items in a bag as a single outcome. Each item is a separate outcome, even if two look the same.
- Writing a probability as favourable outcomes over the outcomes that did not happen. A probability is always favourable outcomes over the total number of outcomes.
- Predicting that every outcome will happen exactly the same number of times. Scale each outcome by its own probability. A more likely outcome should happen more often.
- Deciding a coin or die is unfair as soon as one result differs from the expected probability. A small number of trials can differ from the probability just by chance. That does not mean it is unfair.
Learn and practise “Outcomes and relative frequency” in the app →