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Association, Correlation, and Causation: Three Important and Misleading Terms in Research
Abstract
This study investigates three key ideas in statistics: association, correlation, and causation. When two variables change together, it does not necessarily mean that one variable causes the other to change. An association indicates that certain variables tend to appear or vary together, without confirming a direct link. Correlation goes a step further by measuring how strongly and in what direction two variables are linearly related, yet it still does not establish a cause-and-effect connection. Causation is the strongest form of relationship, yet proving it demands well-planned studies that account for potential confounding factors. Understanding the distinctions between these concepts is crucial for accurate data interpretation, sound study design, and the prevention of misleading conclusions.