Confounding variable

Confounding Variable

A Confounding Variable is a type of statistical variable that can distort or confuse the effect of the independent variable on the dependent variable in a statistical analysis. It is an extraneous variable in a statistical model that correlates (positively or negatively) with both the dependent variable and the independent variable.

Definition

A confounding variable is a variable that influences both the dependent variable and independent variable, causing a spurious association. Confounding is a causal concept, and as such, cannot be described in terms of correlations or associations.

Examples

In a study examining the relationship between exercise and heart disease, age could be a confounding variable. Older people may exercise less and also have a higher risk of heart disease. Therefore, it would appear that exercise is associated with heart disease when in fact the association is between age and heart disease.

Control of Confounding Variables

There are several ways to control for confounding variables, including randomization, matching, stratification, and statistical adjustment. Each of these methods has its own strengths and weaknesses, and the choice of method depends on the specific circumstances of the study.

Implications

Confounding variables can lead to erroneous conclusions in research studies. Therefore, it is important to identify potential confounding variables and use appropriate methods to control for their effects.

See Also

• Dependent and independent variables
• Correlation does not imply causation
• Causal inference
• Statistical control
• Spurious relationship

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