Endogeneity refers to a situation in statistical analysis where an independent variable is correlated with the error term in a regression model. This correlation can lead to biased and inconsistent estimates, making it difficult to determine the true effect of the independent variable on the dependent variable. Common causes of endogeneity include omitted variable bias, measurement error, and reverse causality. For example, if education influences income, but income also affects education, the relationship becomes complex and may mislead researchers about the actual impact of education on income.