Final answer:
High correlation between two variables does not imply causation due to the possible presence of confounding variables or other explanations. Correlation coefficients indicate relationship strength but not cause-effect relationships.
Step-by-step explanation:
It is not accurate to say that a high correlation between two variables necessarily means that one is the cause and the other is the effect. Correlation means there is an association between two variables, where changes in one are related to changes in the other. A correlation coefficient, denoted as r, indicates the strength and direction of this relationship. However, correlation alone does not establish causation. There could be a third factor, known as a confounding variable, influencing both variables, creating the illusion of a cause and effect relationship.
For example, a high correlation between ice cream sales and crime rates does not imply that ice cream causes crime. It may be that warmer temperatures lead to an increase in both. Thus, when interpreting correlations, it is critical to consider other potential explanations and remember that establishing causality requires experimental or longitudinal research that controls for other variables.