Estimate probabilities from experiments, judge which estimate is most reliable, pool results, read a relative frequency graph, and use probabilities to predict expected numbers and profits.
What you will learn
- Work out relative frequency and use it to predict
- Explain why more trials give a more reliable estimate, and pool results from several experiments
- Compare results with the expected number to judge whether something is fair
- Work out expected numbers for one event, two events together and a game with prizes
Relative frequency and expected numbers
When the outcomes are not equally likely, estimate the probability from an experiment:
relative frequency = number of times it happened ÷ number of trials
expected number = probability × number of trials
- a spinner lands on blue 36 times in 120 spins
- relative frequency = 36 ÷ 120 = 0.3
- in 500 more spins, expect 0.3 × 500 = 150 blues
An expected number is an estimate, not a promise: the real count will usually be a little different.
Working backwards: if a spinner with 10 equal sections is expected to land on red 240 times in 400 spins, then P(red) = 240 ÷ 400 = 3/5, so 3/5 × 10 = 6 sections are red.
If some frequencies are given and one is missing, the missing one is the total number of trials minus the others. A biased spinner's results, not 1/4 or 1/2, are then the probabilities to use: if 2 and 4 together came up 45 times in 150 spins, expect about 45/150 × 400 = 120 even numbers in 400 more spins.
Each throw of a fair dice is independent of the ones before: after four sixes in a row, the probability of a six next time is still 1/6. The dice has no memory.
The rest of this lesson and its 9 practice questions are locked.
In this lesson
- Relative frequency and expected numbers
- Which estimate is most reliable?
- Fair or biased?
- Expected profit
- Try it: Run an experiment
9 practice questions, marked as you go; about 25 minutes in all.
Key terms
Pool, Last point
In this unit: Probability and statistics: trees, experiments, samples and claims
More probability and statistics: tree diagrams in depth, expected outcomes from experiments, sampling and populations, critiquing charts and claims, and sample spaces in harder contexts.
- Tree diagrams in depth
- Expected outcomes and experiments
- Sampling and populations
- Critiquing charts and claims
- Sample spaces and combined events
All of Probability and statistics: trees, experiments, samples and claims