Discrete data is often counts of something such as the number of coins in pupils’ pockets; the number of peas in a pod; the number of lengths swum in a sponsored swim. However, the important thing is that only particular values can be taken, which may include negative numbers, fractions etc. and not just integers. For example, shoe sizes in the UK include half sizes such as 7
, data associated with costs is a common occurrence of discrete data – so the value of the coins in pupils’ pockets is also discrete.
Non-numerical discrete data is called categorical data, e.g. pupils’ favourite colours, pupils’ pets, types on sandwiches on sale in a shop.
Discrete data may be grouped, e.g. the numbers of pupils with shoe sizes 3–5
, etc. In this case the top end of one interval is not the same as the bottom end of the next interval, but there will be no possible value of the data set falling in the gap.
Continuous data results from measurements of length, mass, time, area etc. Such measurements are almost always recorded to a specific degree of accuracy e.g. a time may be recorded as 43 seconds having been measured to the nearest second – in this case only values such as 42, 43, 44 will occur, but this data is not discrete. It may be organised in touching but non-overlapping groups. For example, the heights of pupils (x cm) can be grouped into 130 < x ≤ 140, 140 < x ≤ 150, etc.