The study compared different regression models when the data is not normally distributed and has varying levels of variability. They found that using a generalized p value instead of the traditional p value gives more accurate results, especially with small sample sizes. The researchers tested this method through simulations and real data analysis, showing that it works well for comparing regression coefficients even when the data doesn't meet the usual assumptions.