When you toss a coin, there is an equal chance of obtaining a head or a tail. But in some cases, instead of using equally likely outcomes you need to use ‘relative frequency’.

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In an experiment or survey, relative frequency of an event is the number of times the event occurs divided by the total number of trials.

For example, if you observed \(100\) passing cars and found that \(23\) of them were red, the relative frequency would be \(\frac{23}{100}\).

The Probability learner guide describes how you can get a more accurate result in surveys of events if you carry out a large number of trials or survey a large number of people.

This bag contains \({3}\) red sweets and \({7}\) blue sweets.

Tom took a sweet from the bag, noted its colour and then replaced it.

He did this \(10\) times and found that he obtained a red sweet on \(4\) occasions, so the relative frequency of the event that a red sweet was chosen is \(\frac{4}{10}\).

He then carried out the experiment another \({10}\) times and combined his results with the first trial. He found that he had obtained a red sweet on \(5\) out of \(20\) occasions, so the relative frequency of the event that a red sweet was chosen was \(\frac{5}{20}\).

Tom continued in this way, recording his combined results after every \({10}\) trials and plotting them on the graph below:

We can see from the graph that relative frequency gets better (ie closer to the true probability) as the number of trials increases.