92526 Chap.4 Data Types and How Computers Store Them
Data Types and How Computers Store Them
Week 2 asks what forms data can take before anything is summarised, and the answer is a short hierarchy that decides most later choices. Categorical data splits into nominal and ordinal; numerical data splits into discrete and continuous; and continuous measures split again into interval scales, with no absolute zero, and ratio scales, which have one.
The chapter draws that hierarchy and works examples from sport for every branch.
It then lines the statistical view up with the computer view: integers, floats, strings and booleans, which are how Excel and programming languages actually store values.
Finally it separates structured data, predefined and searchable in rows and columns, from unstructured data such as video, free text and audio, using the Week 2 sorting activity to show that the label applies to a whole collection.
What this chapter covers
- 01
Categorical and numerical data
- 02
Nominal and ordinal categories
- 03
Discrete and continuous measures
- 04
Interval and ratio scales
- 05
Integers, floats, strings and booleans
- 06
Structured and unstructured data
- 07
Why the type decides the summary and the graph
Worked example · free
Place five athlete variables in the data type hierarchy
- 1(a) Team colour is categorical nominal: labels with no natural order.
- 1(b) Heart-rate zone is categorical ordinal: zone 4 is harder than zone 2, but the steps need not be equal.
- 1(c) Laps completed is numerical discrete: a count with nothing between whole values.
- 1(d) Core temperature is continuous on an interval scale, because Celsius has no absolute natural zero.
- 1(e) Throw distance is continuous on a ratio scale, with an absolute zero, so 80 m is twice 40 m.
Key terms
- Nominal data
- Categories with no natural order, such as nationality or playing position.
- Discrete data
- Numerical values separated by gaps, such as goals scored, where nothing lies between whole counts.
- Interval scale
- A continuous scale with even spacing but no absolute zero, such as temperature in Celsius.
- Float
- The computer type for numbers with integer and fractional parts, used for decimal measurements.
- Unstructured data
- Information kept in its native format, such as video, images, emails or free text, without a predefined table structure.
Data Types and How Computers Store Them FAQ
What is the difference between nominal and ordinal data?
Both are categorical. Nominal categories have no natural order, as with gender or nationality, while ordinal categories do, as with education level or a Likert rating. Ordinal data carries more information than nominal data, because the order itself is meaningful, but the gaps between ordinal categories are not guaranteed to be equal.
How do interval and ratio scales differ?
Both are continuous with consistent intervals between values. An interval scale has no absolute natural zero, so temperature, pH and blood pressure cannot be compared as multiples. A ratio scale has an absolute zero, so height, weight and distance travelled can be described as twice or half as large.
What are integers, floats, strings and booleans?
They are the ways computers store values. Integers are whole numbers without fractional parts, floats are numbers with decimal parts, strings are sequences of characters such as words or codes, and booleans hold one of two outcomes, true or false, often shown as 1 and 0.
What makes data structured or unstructured?
Structured data is organised into a defined format, such as the rows and columns of a spreadsheet or database, which makes it searchable and easy for software to process. Unstructured data, such as video, images, PDFs, emails and audio, does not fit a standard table, is hard to search, and needs processing before analysis, although it is still valuable.
Why does the data type matter before analysis?
The type decides what you can do with a column. Categorical data is summarised with proportions and shown in pie or bar charts, while continuous data is summarised by centre and spread and shown with histograms and boxplots. Storing a value as the wrong computer type, such as a number held as text, also stops formulas working.
Exam move
Build a two-column list of every variable in one of the tutorial datasets, writing its statistical type in one column and the computer type Excel should use in the other, and mark any column where the two disagree. Practise the interval and ratio distinction until you can justify it with the absolute-zero test in one sentence.
Then take three sport datasets from the Week 2 sorting activity in your own words and decide whether each collection is structured or unstructured, explaining what processing would make it searchable. Revisit this list before the summarising chapters, because choosing a statistic or a graph starts with the type.
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