Correlation vs Causation: Differences, Examples and Real Data

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Quick answerCorrelation means two things change together: when one goes up, the other tends to go up (or down). Causation means one thing actually makes the other change. Correlation alone does not prove causation, because a third factor, chance, or reverse direction can explain why two things move together.

A classic example: ice-cream sales and drowning cases rise together every summer. Ice cream does not cause drowning; hot weather increases both. Below, we explain the difference step by step using real data from ScholarsColab.

Correlation vs causation at a glance

Correlation Causation
What it means Two things change together One thing makes the other change
How it is shown A correlation coefficient, a scatter of points An experiment, or careful designs that rule out other explanations
Typical wording “X is associated with Y” “X causes Y”, “X increases Y”
Example Students who sleep more score higher A trial where students are assigned more sleep and then score higher

Example: correlation and causation in ScholarsColab’s data

To explain the difference, we use ScholarsColab’s own data: 41,792 papers, mostly published between 1998 and 2026, by 967 ScholarsColab mentors (data updated 15 October 2026).

Step 1
Find two things that move together. We compared how old each paper is with how many times it has been cited.

Median citations by publication period2010–201484,574 papers2015–201866,260 papers2019–202177,468 papers2022–2024312,817 papers2025–202605,843 papers
Median citations per paper, by when it was published. Older papers have had more time to collect citations. Source: ScholarsColab Research Methods Index.
Across 39,993 papers, the Spearman correlation between a paper’s age and its citations is 0.33: a weak positive relationship. Older papers tend to have more citations.

Step 2
Read the number. A correlation goes from −1 to +1. Zero means no relationship; +1 means the two always rise together; −1 means one always falls as the other rises. A value around 0.3 is a real but weak relationship: many old papers have few citations and some new papers have many.

Step 3
Ask: is it causation? Here the explanation is simple: an older paper has had more years in which others could cite it. Time causes citations to build up. But age does not guarantee citations, which is why the correlation is only weak.

Step 4
Now a trickier case. Review articles (papers that summarise many earlier studies) tend to be cited more than original research papers. Does being a review cause more citations? Not necessarily. Reviews may simply be published on popular topics, or by well-known researchers. Those are possible third factors, so we can only say reviews are associated with more citations.

Type of paperreview or original
↔
Citationsassociated, not proven cause

Why correlation is not causation

Reason What happens Example
Third (confounding) factor Something else drives both Hot weather raises both ice-cream sales and drowning
Reverse direction Y actually causes X Fitter people exercise more, rather than exercise alone making them fit
Chance A pattern appears by coincidence Two unrelated trends that happen to rise in the same years
Selection Only certain cases are studied Studying only successful companies and finding they all took risks

How to move from correlation towards causation

  1. Run an experiment where you change X yourself and assign groups at random. This is the strongest route.
  2. If you cannot experiment, control for third factors in your design or analysis.
  3. Check the time order: the cause must come before the effect.
  4. Look for a plausible mechanism: how exactly would X change Y?
  5. Look for consistency across different studies, places and groups.

Common mistakes reviewers point out

  • Causal words for correlational results, such as “leads to” or “improves” after a survey.
  • Ignoring obvious third factors, such as age, income or the size of an organisation.
  • Treating a strong correlation as proof. Strength shows how closely two things move together, not why.
  • Using Pearson correlation on skewed data such as citations, where Spearman correlation is more suitable.

Frequently asked questions

What is the difference between correlation and causation?

Correlation means two variables change together. Causation means a change in one variable produces a change in the other. Correlation can exist without causation.

Can correlation ever prove causation?

Not on its own. Causation is usually shown through experiments with random assignment, or through designs that rule out third factors, reverse direction and chance.

What does a correlation coefficient tell you?

It shows the direction and strength of a relationship, on a scale from −1 to +1. Values near 0 mean little or no relationship; values near +1 or −1 mean a strong one.

What is the difference between Pearson and Spearman correlation?

Pearson correlation measures straight-line relationships and suits normally distributed data. Spearman correlation uses ranks, so it suits skewed data, such as citations, and relationships that are not straight lines.

What is a spurious correlation?

A spurious correlation is a relationship between two variables that appears real but is caused by a third factor or by coincidence.

Use this data in your own work

Use this data in your own research. Download the aggregated figures behind these charts (counts, medians and shares by type of paper, period, method and discipline) as a CSV file. Please cite it as:ScholarsColab (2026). ScholarsColab Research Methods Index [Data set]. Vidhilekha Soft Solutions Pvt Ltd. Retrieved 15 October 2026, from https://scholarscolab.com/research-methodology/. Licensed under CC BY 4.0.Download the data (CSV) →

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About the data on this page. The ScholarsColab Research Methods Index is built from the Google Scholar publication lists of PhD researchers who mentor on ScholarsColab, and is updated daily as mentors and publications are added. Methods, designs and paper types are identified from titles, so papers whose titles do not mention them are not counted in those groups. The mentors are not a random sample of all researchers (engineering and computer science are strongly represented), so the figures describe this group of researchers and may not apply to research as a whole.