To ensure that your results are valid, consider the following guidelines when you collect data, perform the analysis, and interpret your results.
 The data should include at least one categorical factor
 The categorical factors must be fixed factors that can be crossed and nested.
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?.
 The response variables should be continuous
 If the response variables are correlated, the MANOVA test can detect multivariate response patterns and smaller differences than are possible with separate ANOVA tests. If the response variable is categorical, your model is less likely to meet the assumptions of the analysis, to accurately describe your data, or to make useful predictions.
 If you have either one response or multiple uncorrelated response variables, you can use Fit General Linear
Model to get similar results.
 If your response variable has two categories, such as pass and fail, use Fit Binary Logistic
Model.
 If your response variable contains three or more categories that have a natural order, such as strongly disagree, disagree, neutral, agree, and strongly agree, use Ordinal Logistic
Regression.
 If your response variable contains three or more categories that do not have a natural order, such as scratch, dent, and tear, use Nominal Logistic
Regression.
 If your response variable counts occurrences, such as the number of defects, use Fit Poisson
Model.
 Each observation should be independent from all other observations

If your observations are dependent, your results might not be valid. Consider the following points to determine whether your observations are independent:
 If an observation provides no information about the value of another observation, the observations are independent.
 If an observation provides information about another observation, the observations are dependent.
 The sample data should be selected randomly

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.
 Collect data using best practices

To ensure that your results are valid, consider the following guidelines:
 Make certain that the data represent the population of interest.
 Collect enough data to provide the necessary precision.
 Measure variables as accurately and precisely as possible.
 Record the data in the order it is collected.
 The model should provide a good fit to the data

If the model does not fit the data, the results can be misleading. In the output, use the residual plots, the diagnostic statistics for unusual observations, and the model summary statistics to determine how well the model fits the data.