In statistics, a confounding variable (also confounding factor, lurking variable, a confound, or confounder) is an extraneous variable in a statistical model that correlates (positively or negatively) with both the dependent variable and the independent variable.
A characteristic that may be distributed differently between the study and control groups and that can effect the outcome being assessed. Confounding may be due to chance or bias.
A factor that is associated with both an intervention (or exposure) and the outcome of interest. For example, if people in the experimental group of a controlled trial are younger than those in the control group, it will be difficult to decide whether a lower risk of death in one group is due to the intervention or the difference in ages. Age is then said to be a confounder, or a confounding variable. Randomization is used to minimize imbalances in confounding variables between experimental and control groups. Confounding is a major concern in non-randomized studies.
Factors that can cause or prevent the outcome of interest, are not intermediate variables, and are not associated with the factor(s) under investigation. They give rise to situations in which the effects of two processes are not separated, or the contribution of causal factors cannot be separated, or the measure of the effect of exposure or risk is distorted because of its association with other factors influencing the outcome of the study.
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