## 5 Results
### Final Results
#### 5.2.2 Error Rates of Visual Variables  
The number of errors (incorrect clicks) and timeouts (when the participant did not answer within the task time limit) were analyzed. Figure 10 shows the number of errors for each visual variable, considering ERROR (blue) and TIMEOUT (orange).  
Figure 8: Bootstrap results with and without partial overlap (\(n=216\)). Text (\(p<0.001\)***) and orientation (\(p<0.05\)*) visual variables showed a difference in significance at alpha = 0.05 due to partial overlap.  
Figure 9: The mean time of the bootstrap results with and without partial overlap (\(n=216\)). Text, shape, lightness, and hue visual variables showed a statistically significant difference because of the use of partial overlap (\(p<0.001\)***).  
The saturation and orientation visual variables had the highest number of ERRORS and TIMEOUT. For ERRORS, the saturation and orientation visual variables showed 40% and 27% of all registered occurrences, respectively. Specifically, the saturation visual variable with the three encoded values scenario presented fewer errors. Moreover, the saturation and orientation variables obtained the highest TIMEOUT occurrences with 23.2% and 43.2%, respectively.  
The results obtained for the saturation visual variable could indicate similar encoded values, confusing the participant in scenarios with more than three different visual coding values.  
Figure 10: Analysis based on the number of ERRORS and TIMEOUTS for each visual variable. The saturation visual variable showed 40% of all errors registered considering all proposed scenarios.  
The accentuated number of TIMEOUT occurrences for the orientation visual variable suggests the necessity to review the entire visual encode proposed for the visual variable. Even for the control group, there were excessive TIMEOUTS, mainly for scenarios with a higher level of partial overlap and more than three different visual values.  
The texture and text visual variables presented 18% and 9% of all ERRORS registered during the tests. For TIMEOUT, the visual variables presented 15.4% and 13.3% of the total occurrences, respectively. In this context, it is essential to highlight the difficulty observed in identifying the \"K\" visual coding value (55% of the total events of TIMEOUT for this visual variable), indicating that this specific visual coding value must be replaced with another one.  
The shape, lightness, and hue visual variables had the lowest numbers of ERRORS during the tests with, respectively, 3%, 1% and 1%. For TIMEOUT, the visual variables presented 3.2%, 0.7% and 0.3% of the total occurrences, respectively. In general, the results support the conclusion that these visual variables are robust to high percentages of partial overlap (70%), even for five different visual encoding values per visual variable. It was possible to identify several errors with the CROSS visual encode, when it corresponded to the target item. The lightness visual variable also showed some ERRORS in a specific scenario with five different values.  
Figure 11 shows the total number of errors (ERRORS+TIMEOUT) for each evaluation scenario (visual variable + overlapping level + subset of different visual encoding values). The scenarios with saturation or orientation and five or four different visual coding values exhibited worse results in accuracy and time.  
Figure 11: Total number of errors (ERRORS + TIMEOUT) for each evaluation scenario (visual variable + overlapping level + subset of different visual coding values).  
The data were ordered from the highest number of errors to the smallest, with 40 errors being the maximum number of errors in one evaluation scenario. During the data analysis, the saturation, orientation, and texture visual variables had the most significant number of errors.