Which statement about multicollinearity is not true?

The statement regarding multicollinearity that is not true is that the presence of multicollinearity always indicates that information is redundant. This is an important distinction to make in statistical modeling. Multicollinearity arises when two or more independent variables are highly correlated, which can indeed complicate the interpretation of regression coefficients. However, it does not necessarily mean that the variables are providing redundant information in a substantive sense.

Variable relationships can be intricate, and multicollinearity can result from meaningful associations among predictors that retain their individual value in terms of the variance they explain in the dependent variable. Even though these variables may be correlated, they can still contribute unique insights, especially when considering their roles in combination with other variables in the model or when the focus is on prediction rather than interpretation. Thus, multicollinearity should be addressed in the modeling process, but it does not automatically imply that there is redundancy that has to be removed.

This nuance helps in understanding the complexities of multicollinearity beyond just statistical outcomes, indicating that careful consideration is needed in applying statistical analyses rather than making blanket assessments based on correlation alone.