Endogeneity refers to a situation in statistical analysis where an explanatory variable is correlated with the error term. This can lead to biased and inconsistent estimates in regression models. Common causes of endogeneity include omitted variable bias, measurement error, and reverse causality, where the dependent variable influences the independent variable. To address endogeneity, researchers often use techniques such as instrumental variables or fixed effects models. These methods help isolate the causal relationship between variables, ensuring that the results of the analysis are more reliable and valid. Understanding endogeneity is crucial for accurate data interpretation in fields like economics and social sciences.