## 5 Results
Hence, taking this into account and, also, that samples are independent, we performed non-parametric Mann-Whitney U tests [48] to evaluate the statistical significance of the differences between the Eg and the Ng. The results of these tests are also shown in Tables 2-4. In these tables, the first column indicates the ID of the analyzed variable listed in Table 1; the second column shows the filter applied to the data (i.e., analyzing all data without a filter, segmenting participants according to their previous theoretical cybersecurity knowledge, segmenting participants according to their age); columns 3 to 8 show the number of subjects, the mean and the standard deviation for the Eg and the Ng; and the last three columns show the results of the Mann-Whitney U tests, where the \"Significance?\" column is set to True if the null hypothesis can be rejected (\(p\)-value \(\leq\) 0.05) and, therefore, consider that the samples from the two groups come from different distributions. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline  & & \multicolumn{3}{c}{**Expository Group (Eg)**} & \multicolumn{3}{c}{**Narrative Group (Ng)**} & \\ \hline
**Filter** & **Variable** & **Count** & **Mean** & **Std** & **Count** & **Mean** & **Std** & **Statistic** & \(p\)**-Value** & **Significance?** \\ \hline No filter & P1.1 & 49 & 4.12 & 0.56 & 62 & 3.90 & 0.74 & 1301.5 & 0.067 & False \\ No filter & P1.2 & 49 & 4.31 & 0.68 & 62 & 4.21 & 0.73 & 1423.0 & 0.27 & False \\ No filter & P1.3 & 49 & 4.04 & 0.61 & 62 & 3.65 & 0.83 & 1100.0 & 0.0035 & True \\ No filter & P1.4 & 49 & 4.22 & 0.62 & 62 & 4.27 & 0.68 & 1433.5 & 0.29 & False \\ No filter & P1.5 & 49 & 2.96 & 1.04 & 62 & 2.97 & 1.04 & 1485.5 & 0.42 & False \\ \hline No Prev. knowledge & P1.1 & 38 & 4.11 & 0.56 & 46 & 3.87 & 0.75 & 745.0 & 0.085 & False \\ No Prev. knowledge & P1.2 & 38 & 4.29 & 0.65 & 46 & 4.15 & 0.76 & 803.0 & 0.24 & False \\ No Prev. knowledge & P1.3 & 38 & 3.95 & 0.61 & 46 & 3.61 & 0.91 & 671.0 & 0.025 & True \\ No Prev. knowledge & P1.4 & 38 & 4.24 & 0.59 & 46 & 4.24 & 0.71 & 850.0 & 0.41 & False \\ No Prev. knowledge & P1.5 & 38 & 2.87 & 1.04 & 46 & 2.98 & 1.00 & 803.5 & 0.26 & False \\ \hline Prev. knowledge & P1.1 & 11 & 4.18 & 0.60 & 16 & 4.00 & 0.73 & 76.0 & 0.26 & False \\ Prev. knowledge & P1.2 & 11 & 4.36 & 0.81 & 16 & 4.38 & 0.62 & 85.0 & 0.45 & False \\ Prev. knowledge & P1.3 & 11 & 4.36 & 0.50 & 16 & 3.75 & 0.58 & 44.0 & 0.0063 & True \\ Prev. knowledge & P1.4 & 11 & 4.18 & 0.75 & 16 & 4.38 & 0.62 & 76.0 & 0.27 & False \\ Prev. knowledge & P1.5 & 11 & 3.27 & 1.01 & 16 & 2.94 & 1.18 & 74.5 & 0.25 & False \\ \hline Age \(<\) 30 & P1.1 & 9 & 4.11 & 0.60 & 11 & 4.09 & 0.70 & 49.0 & 0.5 & False \\ Age \(<\) 30 & P1.2 & 9 & 4.22 & 0.83 & 11 & 4.18 & 0.87 & 49.0 & 0.5 & False \\ Age \(<\) 30 & P1.3 & 9 & 3.89 & 0.60 & 11 & 3.45 & 0.93 & 33.0 & 0.096 & False \\ Age \(<\) 30 & P1.4 & 9 & 4.00 & 0.71 & 11 & 4.45 & 0.52 & 32.0 & 0.073 & False \\ Age \(<\) 30 & P1.5 & 9 & 2.56 & 0.88 & 11 & 2.18 & 0.87 & 39.0 & 0.21 & False \\ \hline Age \(\geq\) 30 and \(<\) 40 & P1.1 & 21 & 4.14 & 0.65 & 28 & 4.00 & 0.54 & 258.0 & 0.2 & False \\ Age \(>\) 30 and \(<\) 40 & P1.2 & 21 & 4.29 & 0.64 & 28 & 4.18 & 0.55 & 263.5 & 0.24 & False \\ Age \(\geq\) 30 and \(<\) 40 & P1.3 & 21 & 4.05 & 0.59 & 28 & 3.82 & 0.82 & 247.5 & 0.15 & False \\ Age \(\geq\) 30 and \(<\) 40 & P1.4 & 21 & 4.33 & 0.58 & 28 & 4.25 & 0.75 & 286.0 & 0.43 & False \\ Age \(\geq\) 30 and \(<\) 40 & P1.5 & 21 & 3.19 & 1.03 & 28 & 3.00 & 0.94 & 271.5 & 0.32 & False \\ \hline Age \(\geq\) 40 & P1.1 & 19 & 4.11 & 0.46 & 23 & 3.70 & 0.93 & 166.0 & 0.065 & False \\ Age \(\geq\) 40 & P1.2 & 19 & 4.37 & 0.68 & 23 & 4.26 & 0.86 & 210.5 & 0.42 & False \\ Age \(\geq\) 40 & P1.3 & 19 & 4.11 & 0.66 & 23 & 3.52 & 0.79 & 131.5 & 0.0088 & True \\ Age \(\geq\) 40 & P1.4 & 19 & 4.21 & 0.63 & 23 & 4.22 & 0.67 & 216.0 & 0.48 & False \\ Age \(\geq\) 40 & P1.5 & 19 & 2.89 & 1.10 & 23 & 3.30 & 1.06 & 172.0 & 0.11 & False \\ \hline \hline \end{tabular}
\end{table}