## 3 Results
Normality tests are especially important when examining empirical data, because they direct research in the right way when choosing statistical methods for data analysis. Namely, if empirical data do not follow the Normal distribution, nonparametric tests are more suitable than parametric ones, especially if data groups are compared (43,44). A Quantile-Quantile (Q-Q) plot was used as a graphical method of comparing the corrosion rate data sets obtained for the NiTi-1 and NiTi-2 alloys in three seawater environments with Normal distribution. Q-Q plots are graphing representations of sample order statistics against some \"expected\" quantiles from a standard normal distribution (43). This graphical method is based on the fact that any significant deviation from linearity will indicate that the data cannot be characterised with a normal distribution. The obtained graphs comparing the samples with the normal distribution are shown in Figure 9. The first row of graphs shows three Q-Q plots for corrosion depth rate related to the NiTi-1 alloy in air, tide and sea environments, respectively, while the second row of graphs depicts the obtained graphical result of corrosion rate analyses for the NiTi-2 alloy in air, tidal and sea environments, respectively. Since in all six cases the pattern is not laying on the line \(y=x\), we cannot claim that our samples follow a Normal distribution. Figure 8: Linear model for the NiTi-2 alloy. Figure 7: Linear model for the NiTi-1 alloy. To confirm the assumption made based on a visual examination of corrosion rate measurements, statistical normality tests were performed additionally. Normality tests calculate the probability that a sample was extracted from a normal population. More specifically, the Shapiro-Wilk normality test was chosen because of the relatively small number of observations [45]. In total, six Shapiro-Wilk tests were performed, with the null hypothesis claiming that each individual set of corrosion rate measurements followed a normal distribution. When performing the Shapiro-Wilk tests, the used hypotheses are:  
**H0. **: _Sample data do not differ significantly from a normal population._  
**Ha. **: _Sample data differ significantly from a normal population._  
Table 6 summarises the results of six Shapiro-Wilk normality tests. The first row of Table 6 shows the calculated values of the test statistics, while the second row shows the corresponding \(p\)-values. The significance level \(\alpha=0.05\) was selected in all tests. As is shown in Table 6 small \(p\)-values were obtained, significantly lower than the value for \(\alpha\). This indicates that there was very little risk of error when rejecting the null hypothesis. The risk to reject the null hypothesis H0 while it is true is lower than 0.01%, 0.01%, 0.48%, 0.01%, 0.01% and 0.02%, respectively. Based on the results of the Shapiro-Wilk tests, it became clear that the measured corrosion rate values in all observed seawater environments for both NiTi alloys did not follow the Normal distribution. \begin{table}
\begin{tabular}{c c c c c c c} \hline  & \(d_{\alpha}^{(1)}\) & \(d_{t}^{(1)}\) & \(d_{s}^{(1)}\) & \(d_{\alpha}^{(2)}\) & \(d_{t}^{(2)}\) & \(d_{s}^{(2)}\) \\ \hline W & 0.775 & 0.830 & 0.945 & 0.807 & 0.900 & 0.918 \\ \hline \(p\)-value & \textless{}0.0001 & \textless{}0.0001 & 0.0048 & \textless{}0.0001 & \textless{}0.0001 & 0.0002 \\ \hline \end{tabular}
\end{table} 
Table 6: Shapiro-Wilk test results. Figure 9: Q-Q plot of NiTi-1 and NiTi-2 alloy samples against Normal distribution. A heavy-tailed distribution has a tail that is heavier than an exponential distribution [46], and, thus, such type of distributions converges slowly to zero. As the Q-Q graphs suggest (Figure 9), the distribution centres were well explained by the normal distributions, but the tails of the empirical data showed a tendency to deviate. Figure 9 shows that the empirical corrosion rate data had heavy tails on both sides, and are therefore not modelled well by a normal distribution.