Which of the following is NOT true concerning correlation?

Correlation, fundamentally, describes a statistical relationship between two variables, indicating how one variable may change in relation to the other. However, while correlation can suggest that a relationship exists, it does not imply that one variable causes the other to change; this is a common misconception. The phrase "correlation does not imply causation" underscores the idea that just because two variables are correlated (meaning they move together), it doesn’t mean that changes in one variable lead to changes in the other.

The ability of correlation to indicate relationships and assist in making predictions relies on identifying patterns between the variables. For example, if two variables show a consistent correlation, it is possible to use that relationship to make informed guesses about future events or measurements.

The concept of correlation impacting the strength of relationships refers to how the degree of correlation can either reinforce or weaken perceived associations between the variables. A strong correlation indicates a more reliable relationship than a weak one, but again, this does not denote causation.

Thus, stating that correlation can show cause and effect is incorrect, as correlation alone cannot establish that one variable directly impacts another, making this the correct choice in this context.