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 inefficiencies in estimating the relationships between variables, particularly in regression analysis. When heteroscedasticity is present, it can affect the reliability of statistical tests and confidence intervals. To address this issue, analysts may use techniques such as transforming the data or applying robust standard errors. Recognizing and correcting for heteroscedasticity is crucial for accurate modeling and interpretation of results in fields like economics and social sciences.