Confounding variables are variables that influence both the dependent and independent variables, causing a spurious association. They are also known as confounders, confounding factors, extraneous determinants, or lurking variables. Confounding variables are a type of extraneous variable that are related to a studys independent and dependent variables. They can be correlated with the independent variable and causally related to the dependent variable. Confounding variables can affect other variables in a way that produces spurious or distorted associations between two variables. Confounding variables are threats to internal validity. Confounding is defined in terms of the data generating model, and to estimate the effect of the independent variable on the dependent variable, the statistician must suppress the effects of extraneous variables that influence both variables. Confounding variables can be controlled by adjustment if and only if there is a set of observed covariates that satisfies the Back-Door condition. The existence of confounding variables in studies makes it difficult to establish a clear causal link between treatment and outcome unless appropriate methods are used to adjust for the effect of the confounders.