Confounding
In statistics, a confounding variable (also confounding factor, hidden variable, 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. Such a relation between two observed variables is termed a spurious relationship. In the case of risk assessments evaluating the magnitude and nature of risk to human health, it is important to control for confounding to isolate the effect of a particular hazard such as a food additive, pesticide, or new drug. For prospective studies, it is difficult to recruit and screen for volunteers with the same background (age, diet, education, geography, etc.), and in historical studies, there can be similar variability. Due to the inability to control for variability of volunteers and human studies, confounding is a particular challenge. For these reasons, experiments offer a way to avoid most forms of confounding.
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Famous quotes containing the word confounding:
“Murder in the murderer is no such ruinous thought as poets and romancers will have it; it does not unsettle him, or fright him from his ordinary notice of trifles: it is an act quite easy to be contemplated, but in its sequel, it turns out to be a horrible jangle and confounding of all relations.”
—Ralph Waldo Emerson (1803–1882)