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The principles of association and causality are frequently misunderstood in scientific study. Although researchers have been aware of this conceptual distinction for some time, it is nonetheless common practise to analyse biomedical data improperly and assert causal correlations. The underlying premise of this discussion is that establishing a causal relationship cannot be accomplished by following a magic formula, and more crucially, that there cannot be causality without a causality theory. Theory, data, and statistics are three crucial components that determine whether causal analyses are successful. In order to speculate on causative mechanisms, the direction of causalitypaths, and intricate interactions between numerous variables, we must first have a theory of causation. Data quality is crucial since some of the underlying assumptions of statistical methods could be flawed
