- 28.11.2025
The crucial difference between correlation and causation: Your guide to avoiding the biggest fallacy in data analysis
Introduction: Why does the human mind confuse correlation with causation? The human mind is naturally inclined to look for patterns and explanations. When we observe that two events A and B occur together repeatedly, the first thing that comes to mind is that one causes the other. This innate tendency is at the core of the confusion between the concepts of Correlation and Causation. In today's world of Big Data and fast-paced information, distinguishing between these two concepts is not just an academic exercise, but an absolute necessity to make sound decisions in finance, health, marketing, and even in our personal lives. Rushing to infer causality from mere coincidence or correlation can lead to wrong strategies and wasted resources, or to the adoption of unscientific beliefs. The importance of distinction in everyday life and scientific research: The hidden dangers of assuming causation from mere correlation Failure to distinguish between the two lies behind many common logical fallacies and misunderstandings. If we assume that correlation is evidence of causation, we may focus on addressing symptoms or coincidental phenomena rather than the root cause of the issue. For example, we may observe that students who wear branded hats get high grades (correlation) and assume that buying the hat will improve our grades (causation), when the real reason is that these students may be from families [...].
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