A variable that co-varies with the independent variable
(IV) making it impossible to discern what is affecting the dependant variable
(DV) is called a confounding variable or a confound.
For example a researcher studying the effects of aircraft noise (IV) on infant mortality (DV) might find that the nearer one lives to a noisy airport the higher the rate of infant mortality becomes.
Unfortunately other variables such as inhalation of jet exhaust and reduction in property value (which may result in residential occupancy of people from a lower socio-economic class who may not be able to afford the same level of medical care as people from surrounding areas) will also co-vary with proximity to the airport and thus exposure to aircraft noise (IV).
It then is becomes impossible to discern from this study which variable is affecting the DV, aircraft noise, jet exhaust, or socio-economic class.
Understanding confounds has practical importance for anyone trying to evaluate sometimes persuasive "scientific" claims. For example some might state they have made statistical analyses that "prove" that people of a certain ethnicity have a lower IQ or commit more crimes than persons of another ethnicity. However it often turns out that many confounds (access to social services, poverty, quality of education, and other social and environmental factors) have not been controlled for and when they are IQ and propensity for criminal activity become relatively equal. So in analysing confounds we can often reveal pseudoscientific claims.
Other types of confounds include experimenter bias and participant bias such as the Hawthorne effect.