1. http://www.mathsisfun.com/fraction s.html Year 8 Mathematics Probability

2. Learning Intentions – Understand the terms: experiment, outcome, event, probability and equally likely – place events in order of likelihood – understand the probability scale – understand why repeating the same experiment does not always give the same result – calculate the probability of an event occurring

3. Key Terms If you roll an ordinary die, there are 6 possible outcomes The act of rolling the die is called an experiment or trial The score you get is called an outcome or an event The list 1, 2, 3, 4, 5 and 6 are the possible outcomes

4. The Probability Scale Probability is a measure of how likely an event is to happen Maths uses numbers to describe probabilities Probabilities can be written as fractions, decimals or percentages You can also use a probability scale, starting at 0 (impossible) and ending at 1 (certain).

5. Calculating Probability If all the outcomes of an experiment are equally likely, then the probability of an event A happening is given by this formula For example the probability of tossing a coin an getting a head is – P(Head)=

6. Calculating Probability If there is more than one possibility of an outcome occurring, the probability of selecting that outcome is increased The probability of selecting a red counter from a bag containing two red counters, one green counter and one yellow counter is P(red) = =

7. Experimental Probability When an experiment is carried out the number of outcomes are not the same each time If you roll a die 60 times, sometimes you might get 9 twos and sometimes you might get 12 twos We calculate the experimental probability as: – P(outcome) =

8. Expected Outcomes If an experiment is repeated the expected number of outcomes can be calculated from the probability For example: – If you roll a die 60 times, we would expect to roll a two, of the times – We would expect to get x 60 = 10 twos – No of Outcomes = P(outcome) x no of experiments

9. Bias Bias occurs when one outcome regularly occurs more often than another For example: – if we roll a die 60 times we would expect there to be 10 sixes – If we get 20 sixes we would expect the die to be biased – The die could be heavier on the side opposite the six