Regression vs correlation compares approaches that may look interchangeable but answer different questions or rely on different assumptions. The right choice depends on the decision, population, data, realism and cost of error.
Side-by-side comparison
| Dimension | Regression | correlation |
|---|---|---|
| Primary purpose | Model an outcome from predictors | Measure association between variables |
| Evidence form | Outcome, predictors and explicit functional form | Paired observations |
| Main strength | Effect estimation and prediction | Simple strength and direction summary |
| Main risk | Misspecification and confounding | No causal interpretation |
How to choose
Model an outcome from predictors and its assumptions fit the intended population and decision.
Measure association between variables and its assumptions fit the intended population and decision.
A method used in the previous study is not automatically the right method for the next decision.
State what different outcomes will cause the organisation to do before seeing the result.
When a combined design is stronger
Many comparisons are false binaries. One method may establish structure or prevalence while another explains context, trade-offs or mechanisms. A sequential design is useful when the first stage improves the instrument, alternatives or interpretation of the second.
Common comparison mistakes
- Comparing labels while ignoring different estimands, populations or task formats.
- Assuming the method with more data is automatically more valid.
- Using cost or speed as the only selection rule.
- Combining outputs that were generated under incompatible definitions.
Selection checklist
This reference is written by Ninth Atlas as a decision-oriented explainer. It separates definition, design, analysis and limitations so a method is not mistaken for an answer. Final study specifications should be reviewed against the actual population, evidence, risk and regulatory context.