Final answer:
The statement 'correlation does not prove causation' highlights that an observed relationship between two variables does not confirm that one causes the other.
Step-by-step explanation:
The phrase "correlation does not prove causation" means that just because two variables are correlated (one variable appears to change with changes in the other), this does not necessarily mean that changes in one are the cause of changes in the other. There could be multiple scenarios explaining their relationship. For instance, they could be impacted by an external 'confounding variable', or their correlation could be coincidental or due to chance.
Understanding the difference between correlation and causation is crucial in scientific research, particularly in the interpretation of observational studies. Correlations are simply observations of associations between variables, measurable by means of a correlation coefficient (r). This coefficient ranges from -1 to 1, indicating the direction and strength of the association, but it does not establish a definitive cause-and-effect relationship.
To establish causation, experimental research where variables are deliberately manipulated is typically required. This helps to eliminate the influence of confounding variables. Thus, when observers commit the 'correlation-causation fallacy', they mistakenly infer a causal link solely based on an observed association, without adequate evidence.