This study exposes the flaw in defining endogeneity bias by correlation between an explanatory variable and the error term of a regression model. Through dissecting the links which have led to entanglement of measurement errors, simultaneity bias, omitted variable bias and self-selection bias, the flaw is revealed to stem from a Utopian mismatch of reality directly with single explanatory variable models. The consequent estimation-centered route to circumvent the correlation is shown to be committing a type III error. Use of single variable based ‘consistent’ estimators without consistency of model with data can result in significant distortion of causal postulates of substantive interest. This strategic error is traced to a loss in translation of those causal postulates into multivariate conditional models appropriately designed through efficient combination of substantive knowledge with data information. Endogeneity bias phobia will be uprooted once applied modelling research is centered on such designs.