Hi @AcceptanceLamb4 : Can you provide the output and the model you used? You’ve over-parameterized your model in some way, but without more knowledge of your data and model it will be hard to diagnose.
This happens, for example, when one of your factors only has one level; that factor is then redundant and should not be included in the model. It can be caused by interactions (where some combinations of the factors are not present in the data) as well.
It can also be caused by collinearity. I.e., one factor is a linear combination of other factors.
impossible to know what’s at work in your case without more information about the data and model.