This study investigates how reliability (internal consistency) affects model-fitting in maximum likelihood exploratory factor analysis (EFA). This was accomplished through an examination of goodness of fit indices between the population and the sample matrices. Monte Carlo simulations were performed to create pseudo-populations with known parameters. Results indicated that the higher the internal consistency the worse the fit. It is postulated that the observations are similar to those from structural equation modeling where a good fit with low correlations can be observed and also the reverse with higher item correlations.