Correlation
A statistical tendency for two things to change together — which need not mean one causes the other.
At a glance
Key signals
0% cross fields · reaches 13 more
- Statistics
- Data Science
- Probability
- Explanation
- Examples
- Misconception
- Sourced relations
- Attribution
Dependencies
What this concept builds on and what it makes possible — derived from the atlas’s dependency, causal and structural relations, not from every related edge.
Foundations · builds on
Confounding variablecausesCorrelationEstablished
A confounding variable can create a misleading correlation.
Mechanism: When a hidden factor drives both measured things, they rise and fall together though neither affects the other — summer heat lifts both ice-cream sales and swimming.
Structural role & consequence
Interpreted from the current atlas graph — what the connections mean, not just how many there are.
Currently dark in the atlas: no key date stored · 5 of 5 of its relations lack claim-level evidence.
atlas representation · Describes the current Thinking OS representation, not the state of the world.
Builds on 1 foundation (requires / depends-on / derived-from / emerges-from).
structural · Structural graph analysis — not a claim of importance, causation or history.
Exercises 3 annotated mental models — a concept that connects several thinking patterns.
curated · Curated annotations, not a derived measure.
Structural neighbourhood: 5 → 42 → 156 concepts reachable within 3 hops.
structural · Structural reach — being reachable is not the same as being understood.
cross-field
5 within-field, 0 cross-field
Strengths & constraints
Constraints
- Evidence coverage currently thin in the atlas — few of its relationships carry claim-level evidence. atlas representation
- No dated history stored — the atlas records no key date for this concept. atlas representation
Conditions
- Its dependency reading rests on 1 foundation relation. structural
- Read structurally — most of its relationships carry no external evidence yet, so claims here are graph-derived. structural
Dependency radial
What this concept builds on (left) and what it makes possible (right) — derived from dependency and causal relations.
Seen through each discipline
How this concept sits in each of its fields — derived from its real connections in the graph, not asserted.
Through this lens it connects to Probability, Regression analysis, Risk and Causation.
Through this lens it connects to Probability and Confounding variable.
What it looks like
Concrete cases that make this idea recognisable in the real world.
Ice cream and drowning
Ice-cream sales and drownings rise together, but neither causes the other — hot summer weather drives both. The confounder, not a causal link, explains the correlation.
Check yourself
A quick check against a common misconception. Nothing is scored — picking the tempting-but-wrong answer just flags an idea worth revisiting.
Which statement is correct?
If two things rise and fall together, one causes the other.
Correlation can arise from a hidden common cause, coincidence, or reverse causation. Establishing cause needs more — a mechanism, an experiment, or careful control of confounders.
Related ideas to explore
Concepts that look related but are not yet connected here — candidates for a connection to reason about, not established links.
This idea also appears in…
The same structure shows up in other disciplines. These are real recurrences drawn from the graph — a starting point for asking “what carries over, and what changes?”
Cause and effect31 disciplines · 31 concepts
Probability23 disciplines · 21 concepts
Signal vs noise14 disciplines · 11 concepts
Concepts
shares a mental model · shares a mental model
shares a mental model · shares a mental model
shares a mental model · shares a mental model
shares a mental model · explicitly analogous
shares a mental model · shares a mental model
shares a mental model · shares a mental model
Correlation means two measurements rise or fall together often enough to notice. It is a clue, not a verdict: a hidden common cause can link two things that do not affect each other at all.
Mental models at work here
Cause and effect
One thing genuinely bringing about another — as opposed to two things merely moving together. Establishing it needs a mechanism and controls, not just a pattern.
Ask: if I changed the cause, would the effect change? And what else might explain the link?
Probability
A way to reason about uncertainty by assigning each possible outcome a share of the whole, between impossible (0) and certain (1).
Instead of 'will it happen?', ask 'how often would it happen if this repeated many times?'
Signal vs noise
Real data mixes a meaningful pattern (signal) with random variation (noise); the skill is telling them apart before you act on either.
Ask how much of what you see could be chance. Small samples are mostly noise; averaging and repetition pull the signal out.
- Connects 5 other ideas across 4 disciplines.
- A cross-disciplinary bridge — its connections reach into 13 other fields.
- Most of its connections are of the “Explains & models” kind.
- It exercises 3 reusable thinking patterns.
Derived from the graph’s real structure — observations, not a score.
Sources
- Comparing Statistics: Correlation (1996) verified
- Regression and correlation verified