Multicollinearity in the Presence of Errors-in-Variables Can Increase the Probability of Type-I Error

Multicollinearity, especially in combination with errors-in-variables, can increase the likelihood of a Type-I error by inflating the value of the estimated coefficients by more than it magnifies their standard errors, thereby increasing the likelihood of obtaining statistically significant results. This anomalous result may be due to an interaction effect between errors-in-variables and multicollinearity.