## 3 Results and Discussion
### Analysis of Features
#### 3.3.1 Independent Samples Statistic
Given the conceptual relation to BF\({}_{10}\) (Bayes Factor--alternative over null), this would suggest moderate evidence favouring the alternative hypothesis, i.e., using data analysis tools in fault detection and diagnosis improves the time between failures of fluid power systems. However, the key term in VS-MPR is \"maximum\" odds, which can presumably be ranked between \"anecdotal\" and \"moderate\" evidence in the Bayes realm, even though the traditionally reported threshold in frequentist inferential statistics (\(p\)-value \(<0.05\)) suggests the existence of a statistically significant result. Regarding the usage of LCMI instruments in terms of performance metrics (Table 6), statistically significant differences are lacking. Namely, although the \(t\)-test suggests a significant result (\(p\) = 0.037) in reducing WOM, the Brown-Forsythe test (\(F\) = 3.763, \(p\) = 0.055) suggests a potential violation of homogeneity of variances (Table 6), which, in turn, looking at [PERSON]'s \(t_{w}\) = \(-1.722\), the results indeed fail to reject the null (\(p\) = 0.092). Finally, looking at the results of MTTR, the non-parametric \(U\) test = 1663,5 (\(p\) = 0.032, RBC = 0.251) suggests significant results. However, given the diagnosticity of \(p\)-value by VS-MPR = 3.312 and \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Metric** & **Test** & **Value** & \(p\) & **VS-MPR** & **SE Diff.** & **Effect** & **95\%CI\({}_{\text{L}}\)** & **95\%CI\({}_{\text{U}}\)** \\ \hline WOM & Student & \(-0.488\) & 0.627 & 1.000 & 335.082 & \(-0.155\) & \(-0.777\) & 0.468 \\  & [PERSON] & \(-0.305\) & 0.766 & 1.000 & 535.847 & \(-0.116\) & \(-0.738\) & 0.511 \\  & M-W. & 669.5 & 0.214 & 1.116 & & 0.217 & \(-0.139\) & 0.524 \\ MTTR & Student & 0.542 & 0.589 & 1.000 & 0.794 & 0.172 & \(-0.451\) & 0.795 \\  & Welch & 0.774 & 0.450 & 1.000 & 0.556 & 0.200 & \(-0.430\) & 0.823 \\  & M-W. & 611.0 & 0.546 & 1.000 & 0.111 & \(-0.245\) & 0.440 \\ MTBF & Student & \(-2.180\) & 0.031 & 3.386 & 233.773 & \(-0.693\) & \(-1.320\) & \(-0.062\) \\  & Welch & \(-2.519\) & 0.025 & 3.964 & 202.317 & \(-0.741\) & \(-1.412\) & \(-0.048\) \\  & M-W. & 315.0 & 0.020 & 4.624 & & \(-0.427\) & \(-0.674\) & \(-0.095\) \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Independent sample \(t\)-test statistic DAT. Figure 6: Independent sample test statistic considering (**a**) comparison of MTBF_Est between data analysis tools (1 = Used; 0 = Not used) in fault detection and diagnosis. The Shapiro-Wilk indicates skewness in MTBF_Est_0 (\(W\) = 0.964, \(p\) \(<\) 0.008) while MTBF_Est_1 (\(W\) = 0.921, \(p\) \(<\) 0.329) suggests typically distributed values, the Brown–Forsythe test suggests equality of variances. The comparison of (**b**) MTTR_Est considering LCMI (1 = Used; 0 = Not used) indicates the equality of variances (F = 1.417, \(p\) = 0.237) but a violation of normality in both cases (\(p\) \(<\) 0.001). the presence of high skewness (Figure 6b), the results can be inconclusive or rather biased. Relying simply on a non-parametric comparison of ranked scores, the study does not offer strong evidence for rejecting the null. Lastly, from the analysis of laboratorial analysis as a part of LCM practice, the study failed to capture the statistically significant effect (Table 7). Namely, although the \(t\)-test reports the presence of an effect (\(p=0.008\), VS-MPR = 9.612), the sample size of only three companies utilising laboratory analysis in their LCM practice does not offer enough evidence to support the findings, primarily since [PERSON]'s test reports nonsignificant results (\(t_{w}=-0.956\), \(p=0.440\)). In conclusion, the analysis of FAP suggests that outsourced personnel for performing fault and failure diagnosis contributes to a reduction in fluid waste and improvement of operational time, but only considering the time between failures. The study lacks evidence that outsourced failure analysis reduces machine downtime, i.e., MTTR.