Before using parametric tests, the assumption of normality was verified using the Shapiro-Wilk W-test.
Due to the lack of conformity of most distributions to the normal distribution (verified using the Shapiro-Wilk test), the non-parametric Mann Whitney U test was used to compare parameters between groups.
Generated FE models were verified using the existing experimental data, FE results, and parametric equations.
The parametric studies employed the numerical model ENVI-met, verified using field measurements, to simulate 33 cases with different combinations of factors.
Normal distribution of continuous variables was verified using the Shapiro-Wilk test and homogeneity of variances was determined using Fisher's F-test and Levene's test to ensure the assumptions were met for use of parametric tests.
D'Agostino and Pearson omnibus normality test (GraphPad) was used to verify the normality of each dataset in this study before using parametric statistics; otherwise, non-parametric statistics have been applied.
Before using parametric statistical procedures, the assumptions of normality, homogeneity and sphericity were verified.
Differences between groups were evaluated using parametric or non-parametric test as appropriate (χ2 or Fisher's test for categorical variables, and analysis of variance or Mann–Whitney U-test).
In cases where both parametric and nonparametric methods are applicable, statisticians usually recommend using parametric methods because they tend to provide better precision.
Data were analyzed using parametric and non-parametric statistical methods.
