Heteroscedasticity refers to a situation in statistical modeling where the variability of the errors or residuals is not constant across all levels of an independent variable. In simpler terms, it means that the spread or dispersion of the data points changes as the value of the predictor variable changes. This can lead to inefficient estimates and affect the reliability of statistical tests. In regression analysis, ordinary least squares (OLS) assumes that the residuals are homoscedastic, meaning they have constant variance. When heteroscedasticity is present, it can violate this assumption, potentially leading to biased results and incorrect conclusions about the relationship between variables.