The science of statistics deals with, among other things, the problem of calculating in advance the ‘probability’ of the occurrence of future events. ‘Probability‘ is a word used by statisticians to mean ‘1ikelihood‘ or ‘chances‘. The concept of probability is easy to understand if we start with simple examples. Probability is usually measured in terms of percentage or in terms of one chance in so many (e.g. 1 in 100). The probability of a tossed coin turning up head is 50 per cent (or 1 in 2) because there is a one in two chances of it being head or tail (unless it is a bent coin). The probability of striking the first prize in a four digit forecast draw is 0.01 per cent (or 1 in 10,000) because there are 10,000 (0000 to 9999) numbers all of which have an equal chance of being drawn and only one is right. However, one can make probability forecasts for more difficult events such as future share prices or the probability of rain tomorrow.

Thus, one can speak of the probability of it raining tomorrow meaning the likelihood or chances of it raining tomorrow. Or one can Speak of the probability of a share selling at a certain price a year from now. Thus, a meteorologist, who is usually also trained as a statistician, may say that during the rainy season, there is a 75 per cent probability (or 3 in 4 chances) of rain occurring the next day.

Let us look at this simple statement further for it is not as simple as it iirst appears. The occurrence of rain is a fairly clear cut issue. It either rains or it does not. It is not possible to have a 75 per cent rain. 80 why is it that the meteorologist speaks of a 75 per cent probability of it raining? This is the first important point about statistics; probability forecasting is not about certainty.