## 2 Materials and Methods
### Analysis of Results  
The statistical analyses were performed with IBM SPSS statistical software (v23 for Windows). Descriptive statistics were analyzed, which allowed conclusions to be drawn about the best- and worst-valued technologies in the perception of different design features and about the importance provided to each of these perceptions.  
The data from the questionnaires could not be considered to follow a normal distribution, based on the Kolmogorov-Smirnov test. Therefore, to check whether there are any statistically significant differences between technologies, non-parametric Friedman tests (non-parametric repeated measures) were applied. The ratings on the perception achieved for each product feature were considered as dependent variables. The display technology was considered as the independent variable. As half of the participants used AR and the other half used VR, the analysis compared 2D, 360\({}^{\circ}\), and AR technologies for half of the sample, and for the other half the technologies compared were 2D, 360\({}^{\circ}\), ni_VR, and i_VR (note that ni_VR and i_VR have been treated as two different technologies). Pairwise comparisons for the technologies were performed within each group, and significance values were adjusted using the Bonferroni correction for multiple tests. An a priori power analysis was performed (Wilcoxon signed-rank test for matched pairs), considering a power of 80%, error probability \(\alpha\) of 0.05, and an effect size of 0.5 (mean difference of 0.5 and SD of difference = 1) as input parameters. The analysis, established for two tails, showed a total sample size of 35 participants.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline  & \multicolumn{3}{c}{**Comparison Options:**} \\
**Design Features** & **The Real Version, Regarding the Perceptions Obtained through the Display Technologies, Seems to Me:** \\ \hline \hline Dimensions & Bigger & Smaller & The same \\ Weight & Heavier & Lighter & The same \\ Color & Darker & Lighter & The same \\ Shine & Brighter & More matte & The same \\ Texture & Rougher & Smoother & The same \\ Reliability & More reliable & Less reliable & The same \\ Quality & Higher quality & Lower quality & The same \\ Appeal & More attractive & Less attractive & The same \\ \hline \hline  & \multicolumn{3}{c}{**The same**} & **Different** \\ \hline Shape & The same & Different \\ Materials & The same & Different \\ Details & The same & Different \\ Mode of use & The same & Different \\ \hline \hline \end{tabular}
\end{table}
Table 3: Product design features and comparative options after seeing the real product, with respect to the perceptions generated from display technologies while browsing the website.  
Figure 6: Photographs of the actual products.  
Moreover, as the participants that used AR and VR were different, the independent-samples Mann-Whitney U test was applied to identify possible differences between perceptions for AR and ni_VR, and for AR and i_VR.  
To verify possible differences in perception importance and ratings, depending on the product type, independent-sample Mann-Whitney U tests were also applied, considering the perception importance or the perception rating for each technology as dependent variables, and the type of product as the independent one.  
Results from the comparison between the perceptions generated by the real product features and those produced through the interaction with the display technologies (Table 3) were described and represented by means of bar charts. To study differences between them, crosstabs (Chi-square coefficient) were applied for the perceptions, depending on the technologies used.