Multicollinearity reduces the precision of the estimated coefficients, which weakens the statistical power of your regression model. You might not be able to trust the p-values to identify independent variables that are statistically significant.

Why is it bad to have multicollinearity?

However, severe multicollinearity is a problem because it can increase the variance of the coefficient estimates and make the estimates very sensitive to minor changes in the model. The result is that the coefficient estimates are unstable and difficult to interpret.

What is bad multicollinearity?

High/Imperfect/Near multicollinearity occurs when two or more independent predictors are approximately linearly related. This is a common type and is problematic to us. All our analyses are based on detecting and dealing with this type of multicollinearity.