A correlation is a pattern where two measures move together.
A causal claim says one measure makes the other change. In data questions you often have to say which one the evidence supports. This is the last lesson before the mixed practice in development and inequality.
All data and regions here are invented.
How do you separate the two step by step?
- Describe the pattern. Say what rises or falls with what, using figures.
- Name the claim. Is the writer only describing, or saying one causes the other?
- Look for a mechanism. Can you explain how one would lead to the other, in the style of a mechanism chain?
- Test other explanations. Could the cause run the other way, or could a third factor drive both?
- Word it carefully. Use “is associated with” for the pattern and “may contribute to” for the possible cause.
Worked example
In an invented set of eight regions, those with more doctors per 1,000 people also have higher average incomes. A report says: “More doctors cause higher incomes.”
Pattern: a positive correlation. Regions with 3 doctors per 1,000 have incomes around US$15,000, while regions with 1 doctor per 1,000 are around US$5,000. The difference is 15,000 − 5,000 = 10,000.
Mechanism? Healthier workers may earn more, so there could be a link. But it is not clear that doctors alone explain a US$10,000 gap.
Other explanations: richer regions can afford to train and pay more doctors, so the cause may run the other way. Or a third factor, such as investment in a large city, may raise both doctors and incomes.
Careful wording: “Regions with more doctors tend to have higher incomes, but the data alone do not show that doctors cause the higher incomes. Income may fund health services, or other factors may affect both.”
What mistake should you watch for?
Mistaken answer: “The graph proves that mobile phones lengthen life, because regions with more phones have higher life expectancy.”
The student has jumped from a pattern to a cause. Phones and life expectancy may both rise because incomes are higher, and the graph does not show a mechanism from phones to health.
The correction is to say “Regions with more phones per 100 people tend to have longer life expectancy. This is an association. Higher incomes may explain both, so the data do not prove that phones cause longer life.”
Check yourself
All data are invented.
1. Rewrite as a correlation statement: “Countries with more cars per 1,000 people live longer because of cars.”
Show answer
“Countries with more cars per 1,000 people tend to have longer life expectancy. This is an association, and a factor such as higher income may explain both.”
2. Name one way the cause and effect might be reversed in the claim “More schools cause higher incomes.”
Show answer
Higher incomes may give governments and families more money to build and attend schools, so income could be a cause of more schools.
3. In 8 regions, 6 show higher incomes with more doctors, and 2 do not. Is this proof of cause?
Show answer
No. 6 out of 8 is 75 per cent, which is a pattern. It still does not prove cause, because the two exceptions and other explanations remain, and the sample is small.
Where does this lead next?
Test the whole module with the original mixed practice. The statistics and distribution explorer lets you explore patterns before you decide what they show. If you want help phrasing evaluations accurately, our teachers offer online one-to-one Geography tuition.