## 3 Results
### Statistical Analysis of the Experimental Data  
In the first step, all data (length, diameter, and depth) belonging to each class were analysed using Q-Q plots tests, with the aim of seeing the nature of their distribution. In Figure 5, we can see the Q-Q plots tests, performed for length for each of the classes I, II, and III, respectively. Examining these graphs, we can see that most of the points are linearly distributed, which clearly indicates a normal distribution of the respective data. The same can be seen for diameter (Figure 6) and depth (Figure 7), for each of the three classes separately. From In Figure 5, it can be seen that almost all the points are arranged on a straight line, with some minor fluctuations. Thus, we can conclude that they are approximately normally distributed when examining the length of the sella turcica. The same type of arrangement, along a line, can be seen both in Figure 6, for diameter, and in Figure 7, for depth. Thus, all the data associated with the linear dimensions belonging to classes I, II, and III, respectively, follow an approximately normal distribution. Multiple Student's T-tests were used to see if there was a correlation between skeletal class and the linear dimensions of the sella turcica. For all evaluated linear dimensions, Table 4 confirms a statistically significant difference between class I and class III individuals, respectively, class II and class III (\(p<0.001\)). There was no significant difference in the linear dimensions of cases from skeletal groups I and II (\(p=0.133\); \(p=0.138\); \(p=0.567\)). However, since three skeletal classes were used, a statistical analysis between three different groups requires the use of an ANOVA test along with a Bonferroni correction. This type of test can tell us if there is a statistical difference between the three classes used. The reason for using a Bonferroni correction is that, normally, for the three classes (which involve \(n=3\) tests), using a significance level \(\alpha=0.05\), we would obtain a large degree of error:  
\[1-(1-\alpha)^{n}=14.26\%\]  
thus obtaining irrelevant results for rejecting the null hypothesis. Figure 4: The frequency distribution of various sella turcica morphologies in the three skeletal classes. Thus, following the Anova test with Bonferroni correction, it was concluded that the \(p\)-values are much lower than those of the pre-specified significance level (\(\alpha=0.05\)) (Table 4). For all evaluated linear dimensions, Table 4 confirms a statistically significant difference between the length, diameter, and depth of all three classes, due to the fact that a very small \(p\)-value was obtained (below 0.001) compared to the value of the significance level \(\alpha\). In other words, the means obtained for all three classes are not the same. Thus, we can conclude that our experimental study is statistically significant. Figure 5: Q-Q plots for the length, for each of the classes I, II, and III, respectively. One of the limitations of the Bonferroni correction or any other test for controlling family-wise error rate (FWER) is if there are a large number of tests or if the test statistics are positively correlated. Thus, in order to test whether our data are positively correlated, the Pearson correlation coefficient was calculated for length, diameter, and depth corresponding to all combinations of classes. These results are presented in Table 5 and indicate the correlation between the data. For example, a positive correlation can be found for all experimental data in classes II and III, respectively. For other class combinations, there are also values quite close to zero, for example, length and depth between classes I and III and diameter and depth between classes I and II, showing a relatively weak correlation between the experimental data. Taking these values into account, we tend to believe that there is a statistical difference between the classes. Figure 6: Q-Q plots for the diameter, for each of the classes I, II, and III, respectively.