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Multicollinearity occurs when two or more predictors in a regression are so highly correlated that they carry overlapping information about the outcome. Because of this redundancy, the model struggles to disentangle each predictor’s unique effect. The estimated coefficients become highly sensitive to small changes in the data or in which predictors are included, leading to unstable estimates. Standard errors inflate, reducing precision and making it hard to determine whether a predictor truly has an effect. The overall model may fit the data, but interpreting the individual coefficients becomes unreliable. This is why the idea that the regression equation becomes unstable and the coefficients cannot be meaningfully interpreted best captures what multicollinearity does. It does not imply perfect predictive power, it does not make coefficients more precise, and a larger sample size doesn’t by itself fix the problem.