Types of data and variables — Data, 14–17 years
Before analysing data, identify what each value represents and what kind of values it can take.
What data types mean
A variable is a feature that can differ from one person, object or event to another, such as age, eye colour or number of messages. Numerical data use numbers; categorical data place things into groups. Numerical values can also be discrete, like 3 siblings, or continuous, like 1.72 metres.
Why the type matters
The type tells you which comparisons and graphs make sense. You can average heights, but averaging eye colours produces nonsense; for categories, counts or proportions are useful instead. This distinction grew from the need to store and summarise very different kinds of observations without inventing meaning.
A worked example
A sports club records 12 members’ weekly training sessions: 0, 2, 3, 1, 4, 2, 0, 5, 1, 2, 3, 1. First, identify the variable: number of sessions. Second, it is numerical and discrete, because sessions are counted. Third, the mean is 24 ÷ 12 = 2 sessions per week.
The trap: numbers are not always numerical
A code such as 0147 may look like a number, but it can simply identify a library book or a postcode. Adding codes or finding their mean is meaningless. The mistake is reasonable because digits usually invite calculation; ask what the value represents before calculating with it.
Where it is used
In a spreadsheet, choosing the right type helps software sort dates, add prices and count responses correctly. A hospital may treat blood group as a category, temperature as continuous data and number of visits as discrete data. The labels affect both the analysis and the decisions based on it.
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