Heteroscedasticity refers to a situation in regression analysis where the variability of the residuals (errors) varies across levels of an independent variable. In simple terms, it means that the spread or scatter of the data points is not consistent; some areas may have a lot of spread while others are more tightly clustered. This can make it difficult to trust the results of the regression analysis because the assumptions of constant variability (homoscedasticity) are violated, potentially leading to unreliable estimates and predictions.