## 4 Results and Analysis
### Statistical Analysis: Results  
The survey data was used to test patterns observed in the qualitative data. However, it is important to note that the survey was constructed several months before the roundtablediscussions, leading to some variance in topic coverage, especially since the roundtable discussions were unstructured. Nonetheless, many themes are addressed in both methods of analysis.  
Out of 42 survey respondents, only 18 answered in full, which resulted in missing responses, limiting some analyses. A 2012 study by [PERSON] et al. [92] identifies a need for surveys to be designed with engagement in mind, to prevent inattentive or incomplete responses. Hence, this is vital to reflect upon for future research.  
The largest group in terms of years of experience was those with 20+ years of experience (39% of survey respondents), followed by those with less than 2 years of experience (27% of survey respondents). Comparatively, the largest group in terms of experience flying GA in hours is pilots with over 1000+ h (42% of survey respondents), then those with 100-500 h (30% of survey respondents). Additionally, the pilots were asked when the last time they completed a safety course was. Notably, 6% of pilots had never completed a safety course.  
The hours in Table 2 were selected based on stages of licencing: for example, at 20 h, a pilot usually has achieved their solo flight; at 100 h, a pilot usually has their PPL; at 500 h, a pilot has their commercial licence; and at 1000 h, a pilot is eligible to work towards an ATPL [93].  
To check the goodness-of-fit, a Pearson's chi-squared test was conducted, referred to in Tables 3-6. This was done by comparing the survey sample size with Transport Canada's [61] data on how many pilots hold certain types of licences. Since not all participants completed the survey--33 pilots provided information about their licences, whilst only 18 completed the entire survey--the chi-squared test was performed on both sample groups.  
\begin{table}
\begin{tabular}{c c c} \hline
**Licences (33 Pilots)** & **Expected** & **Observed** \\ \hline Recreational Pilot Permit & 0.95 & 0 \\ PPL & 16.16 & 18 \\ CPL & 5.53 & 12 \\ ATPL & 11.37 & 10 \\ \hline \end{tabular}
\end{table}
Table 4: Chi-squared (33 pilots).  
\begin{table}
\begin{tabular}{c c} \hline
**Hours Flown** & **Number of Survey Participants** \\ \hline
1–20 h & 1 \\
20–100 h & 5 \\
100–500 h & 10 \\
500–1000 h & 3 \\
1000+ h & 14 \\ \hline \end{tabular}
\end{table}
Table 2: Experience flying GA (in hours).  
\begin{table}
\begin{tabular}{c c c} \hline
**Hypothesis** & **Statement** \\ \hline H\({}_{0}\) & The observed distribution of licences follows the same distribution as the population of licences \\ H\({}_{1}\) & The observed distribution of licences does not follow the same distribution as the population of licences \\ \hline \end{tabular}
\end{table}
Table 3: Null and alternate hypotheses.  
Comparing this against the number of licences, \(\chi^{2}\) was calculated for both sample groups. For 33 pilots, the \(p\)-value rendered the difference statistically significant, hence H\({}_{0}\) was rejected. However, for the sample of 18 pilots, the \(p\)-value meant H\({}_{0}\) could be accepted. Nevertheless, the smaller sample size limits statistical power [94], which may explain the non-significant result.  
Yet, from these findings, in both cases, the number of CPL and ATPL holders is higher than the expected population, indicating that pilots with more experience dominate this study. This would impact the results, as the input of lower-hour pilots will be underrepresented. Also, it is important to mention that some pilots hold more than one licence, and five of the pilots who are in training have not yet obtained a licence.