The study looked at how different violations of data assumptions affect statistical models used in research. They used simulations to test various scenarios with different group sizes and types of data violations. They found that unbalanced designs often led to unreliable results when data assumptions were violated. However, balanced designs showed more robustness than expected, even with multiple violations like varying group variances and skewed data. This has important implications for educational and social science research.