the situation in which values calculated from a statistical model systematically overestimate or underestimate a degree of relationship or other quantity of interest because an important variable has been left out of the model. For example, a researcher could hypothesize a linear regression equation in which stressful life events and lack of social support predict depression. If coping skills also are highly relevant to predicting depression, the researcher’s failure to include that element in his or her conceptualization would create an omitted variable bias. The exclusion of important variables from models may constrain the validity of study findings. See also biased estimator.