In statistics, a confounding variable (also confounding factor, a confound, a lurking variable or a confounder) is an extraneous variable in a statistical model that correlates (directly or inversely) with both the dependent variable and the independent variable, in a way that "explains away" some or all of the correlation between these two variables. While specific definitions may vary, in essence a confounding variable fits the following four criteria, here given in a hypothetical situation with variable of interest "V", confounding variable "C" and outcome of interest "O": 1. \n* C is associated (inversely or directly) with O 2. \n* C is associated with O, independent of V 3. \n* C is associated (inversely or directly) with V 4. \n* C is not in the causal pathway of V to O (C is not a direct consequence of V, not a way by which V produces O) The preceding correlation-based definition, however, is metaphorical at best – a growing number of analysts agree that confounding is a causal concept, and as such, cannot be described in terms of correlations nor associations (see causal definition).

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