The lack of fit test, when possible, provides some evidence to decide if the estimated errors (residuals) in the current model are entirely random effects or a mixture of random and fixed effects. If it is the latter case, then the problem is model bias. All models should be validated with new empirical observations. Use the selected model to predict the outcome for factor levels / combinations that were not included in the data set that trained the model. Do the predictions confirm?