Correlation measures whether two variables move together. Causation means one variable directly produces a change in another. Confusing them leads to flawed conclusions.
Pearson Correlation
r ranges from -1 to +1. r = 0.9 indicates strong positive association. Calculate with our Correlation Calculator.
Establishing Causation
Requires controlled experiments, temporal precedence, and ruling out confounding variables. Ice cream sales correlate with drowning deaths - both rise in summer (confounding variable: weather).
Regression Analysis
Regression predicts one variable from another but still does not prove causation. See the Regression Calculator.
Going Deeper
Why correlation does not imply causation and how to analyze data correctly. This guide connects theory to practice — use the related calculators linked at the bottom to verify each example with your own numbers.
Practical Tips
- Write down given values and unknowns before opening the calculator.
- Check units and rounding rules appropriate to your context (class, lab, or business).
- Compare manual working with the calculator result to build confidence.
Common Mistakes to Avoid
- Rushing inputs without reading field labels carefully.
- Confusing similar formulas that use different variables or units.
- Reporting results with more precision than your inputs justify.
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