To ensure that your results are valid, consider the following guidelines when you collect data, perform the analysis, and interpret your results.
If your design contains covariates, fixed factors, or crossed factors, use Fit General Linear Model.
For more information on factors, go to Factors and factor levels, What are factors, crossed factors, and nested factors?, and What is the difference between fixed and random factors?.
Nesting does not need to be balanced. A nested factor must have at least 2 levels at some level of the nesting factor. If factor B is nested within factor A, there can be unequal levels of B within each level of A. In addition, the subscripts used to identify the B levels can differ within each level of A. However, if your fully nested design is not balanced, Minitab cannot calculate the F and p-values.
Minitab uses sequential (Type I) sums of squares for all calculations in Fully Nested ANOVA. If you want to use adjusted sums of squares, use Fit General Linear Model.
If your design is not fully nested, use Fit General Linear Model.
Random samples are used to make generalizations, or inferences, about a population. If your data were not collected randomly, your results might not represent the population.
If the model does not fit the data, the results can be misleading. In the output, use residual plots to determine how well the model fits the data.