Which of the following best describes the impact of multicollinearity on regression analysis?

Multicollinearity refers to a situation in regression analysis where two or more independent variables are highly correlated, meaning they provide redundant information. When multicollinearity is present, it can lead to unreliable coefficient estimates. This means that the estimated coefficients of the regression model may become unstable, making it difficult to determine the individual effect of each independent variable on the dependent variable.

As a result, even small changes in the data can lead to large fluctuations in the estimated coefficients, reducing the reliability of hypothesis tests regarding these coefficients and potentially leading to incorrect conclusions about relationships in the data. High multicollinearity does not enhance the predictive power of the model; instead, it complicates interpretations of the model's coefficients and can mislead researchers in understanding the true effects of predictors.

In contrast, simplification of model interpretation and aiding in variable selection or enhancement of predictive power are not characteristics associated with multicollinearity. Instead, these outcomes are generally seen as goals in regression modeling that can be hindered by multicollinearity.