Nobody collects data for its own sake. The guide names two big uses, and almost every real system is doing one of them.
The two uses
Find patterns and links
Spot trends over time, groups that behave alike, and two things that rise and fall together. This is what turns a pile of records into something useful.
Measure people and communities
Collect facts about how people live, move, spend and vote — things that used to be guessed at, asked about in surveys, or simply unknown.
Both uses show up in the same system: A transport app measures where people travel, then finds the pattern in it. The measuring is what makes people uneasy; the pattern is what makes the app work.
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The most common mistake in data-based answers is treating “these two move together” as “one causes the other”. They are different claims and examiners reward students who keep them apart.
A link (correlation)
- Two things rise and fall together
- Ice-cream sales and drownings both peak in July
- Something else — hot weather — may be behind both
- Enough to predict, never enough to blame
A cause
- Changing one changes the other
- Shown by a controlled test, not by a chart
- Needs a reason you can describe, not just a pattern
- Enough to act on
Why it matters here: A system that acts on a link as if it were a cause will treat people unfairly and look right while doing it, because the numbers really do line up.
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These two cases are the same use of data — finding a pattern and acting on it — with very different results.
Real-world examples you can name
Global Fishing Watch — launched 2016
Satellite tracking signals from vessels are processed to map fishing activity worldwide and published openly, so protected areas can be policed by anyone with a browser rather than only by patrol boats.
Who it affected: Coastal states without navies, and fleets that relied on not being watched.
Amazon's scrapped CV-screening tool — reported October 2018
An experimental hiring tool was trained on a decade of CVs submitted to the company, most of them from men. It learned to downgrade CVs containing the word 'women's' and graduates of two women's colleges. The project was abandoned.
Who it affected: Women applying for technical roles, and every organization that assumed historical data was neutral.
The difference worth noticing: Both systems found a real pattern. One was pointed at boats and made a public good. The other was pointed at people, and repeated a past that nobody had meant to keep.
How this is tested — you have to say what a system uses data FOR, and then what that use cannot deliver. It comes up two ways:
Paper 1 — structured question
- Part a: identify uses of the data a system collects
- Part b: explain why a pattern in data may be misleading
Paper 2 — source-based question
- Q1: describe a trend a chart shows
- Q2: explain a claim a source makes from its data
The trap: describing the chart instead of reading it: “Sales went up and then down” repeats what anyone can see. The mark is for what it means — the size of the change, when it turned, and what it is evidence of.
Identify two uses of the data collected by a city's bike-hire scheme.
Model answer plan
See the mark-by-mark plan — for / against / judgement, with marking guidance — in study mode.
Explain one reason why a pattern found in data may not show a cause.
Model answer plan
See the mark-by-mark plan — for / against / judgement, with marking guidance — in study mode.