Multicollinearity in multiple regression refers to a situation where two or more independent variables are highly correlated, meaning that they provide redundant information about the variance in the dependent variable. This high correlation can complicate the estimation of coefficients, making them unstable and sensitive to small changes in the model or data. As a result, multicollinearity can lead to inflated standard errors, which reduces the statistical significance of the predictors and makes it difficult to ascertain the individual effect of each variable on the dependent variable. Identifying and addressing multicollinearity is crucial because it can hinder the interpretability of the regression model, potentially leading to misleading conclusions about the relationships between variables. Techniques such as variance inflation factor (VIF) analysis, removing or combining correlated predictors, or using regularization methods like ridge regression can help mitigate the effects of multicollinearity.