Omitting one or more key variables in an analysis can result in a biased model.
Most models assume that all key variables are included for the original analysis.
A key variable may be removed:
(1) intentionally excluded at outset
(2) accidentally left out at outset
(3) by error during model development
Intentional or accidental omission of a key variable can result in:
(1) incorrect weighting of other variables
(2) a model that gives incorrect results
(3) hypotheses that do not make sense
The possibility of omitted variable bias should always be considered if a model is unreliable or if hypotheses are inconsistent with facts.
