Interpreting correlation helps us understand the strength and direction of a relationship between two variables.
📌 What is Correlation?
Correlation measures how two variables move in relation to each other. It’s usually quantified using Pearson’s correlation coefficient (r), which ranges from -1 to +1.
🔢 Correlation Values and Their Meaning
| Correlation Coefficient (r) | Strength | Direction |
|---|---|---|
| +1.0 | Perfect correlation | Positive |
| +0.90 to +0.99 | Very strong | Positive |
| +0.70 to +0.89 | Strong | Positive |
| +0.40 to +0.69 | Moderate | Positive |
| +0.10 to +0.39 | Weak | Positive |
| 0.00 to +0.09 | Negligible | Positive |
| 0.00 | None (No correlation) | — |
| –0.01 to –0.09 | Negligible | Negative |
| –0.10 to –0.39 | Weak | Negative |
| –0.40 to –0.69 | Moderate | Negative |
| –0.70 to –0.89 | Strong | Negative |
| -0.90 to -0.99 | Very strong | Negative |
| -1.0 | Perfect correlation | Negative |
📈 Types of Correlation
- Positive: As one variable increases, so does the other.
E.g., Hours studied and exam scores. - Negative: As one variable increases, the other decreases. E.g., Stress level and sleep quality.
- Zero: No linear relationship between variables. E.g., Shoe size and IQ.
⚠️ Important Caveats
- Correlation ≠ Causation: Just because two things move together doesn’t mean one causes the other.
- Outliers can distort correlation values.
- Non-linear relationships won’t be captured well by Pearson’s r.
- Spearman’s rank correlation is used for non-linear but monotonic relationships.
✅ How to Use It in Practice
- Look at both the correlation coefficient and the scatterplot.
- Always think about possible confounding factors.
- Use domain knowledge to interpret whether a correlation is meaningful.