“highly significant”

As shown in Table 3 , the increases in F1 scores for all three models reached a highly significant level ( p < 0.001).

The Mann–Whitney U test indicated that this difference was highly significant ( U = 18, Z = −12.6, p < 0.001), with a large effect size (rank‐biserial, r = 0.86).

The similar efficacy trend of edema inhibition observed in both AiME and the standard treatment is supported by a highly significant statistical difference, with p < 0.001 (***) between the two groups. 3.3.

The statistics revealed that the interaction between insertion depth and bone type was highly significant ( p < 0.001) for both torque and ISQ.

Response times The main effect of cue type was highly significant, with F (2, 68) = 116.79, p < 0.001, η p 2 = 0.77.

The difference was highly significant in every case for HRs ( p < 0.001) and in two of four cases for FARs.

The consistent separation of curves across both time periods and all drug types, combined with highly significant log-rank test results (p<0.001 for all comparisons except famciclovir in period 2), provide strong evidence for a protective association between AHD use and AD treatment initiation ( online supplemental figures S1 and S2 ).

Additionally, family history also showed a highly significant difference ( p = 0.001); (56.7%) of cases in the ASD group and (33.3%) of cases in ID group had positive family history of neurodevelopmental disorders in comparison to control group.

The factorability of the data was confirmed by a high Kaiser-Meyer-Olkin measure (KMO = 0.84) and a highly significant Bartlett’s test of sphericity (χ2 = 11,447, p < .001), indicating sufficient intercorrelations among the variables.

All tumors <30 mm achieved complete resection, while piecemeal retrieval was required in 9.6% of 30–50 mm tumors and 89.5% of ≥50 mm tumors. Univariable Analysis In univariable analysis for piecemeal retrieval, both tumor dimensions were highly significant: longitudinal length (OR = 1.35 per 10 mm, 95% CI: 1.16–1.57, p < 0.001) and transverse length (OR = 1.53 per 10 mm, 95% CI: 1.29–1.81, p < 0.001).

Mixed-effects regression The Time × Group interaction was highly significant (B = 2.46, p < 0.001), confirming greater practice gains in the intervention group (+4.24, p < 0.001) vs. controls (+1.78, p < 0.05).

The body ecotype showed highly significant differences (p < 0.001), with a predominance of the longilinear biotype in males (86.1%).

Log-rank (Mantel–Cox) testing indicated highly significant differences between groups ( χ 2 = 83.260, P < 0.001; Supplementary Table 2 ).

Data analysis revealed highly significant seasonal variations (P< 0.001).

VEGF A & TGF β1 measured in arbitrary units (AU): Both growth factors showed highly significant scores in the CTG immersed in i-PRF analyzed in the CTG + i-PRF group, compared to the CTG alone from the CTG group ( P -value < 0.001), with large effect sizes for both growth factors suggesting a substantial magnitude of difference in growth factors expression between groups ( Table 5 ).

There was a highly significant main effect of Time ( p < 0.001), confirming that hemi‐mMASI decreased significantly during follow‐up in both treatment modalities.

However, on the larger external Kaggle dataset ( n = 693), the differences between the ResNet + DenseNet ensemble and the individual models were highly significant: compared to ResNet50V2 ( Z = 4.816, p < 0.001) and InceptionV3 ( Z = 8.042, p < 0.001).

As shown in Table 5 , the results of testing the hypotheses regarding the correlations among factors influencing learning resilience are as follows: academic self-efficacy and perceived campus belonging exhibit highly significant correlations with learning resilience at the P < 0.001 level; learning climate shows less significant correlation with learning resilience at the P < 0.05 level; Social relationships and learning resilience exhibit a significant correlation at the P < 0.01 level; however, the relationship between learning environment and learning resilience is less significant at the P < 0.05 level.

The test for subgroup differences was highly significant (chi 2 = 10.62, p = 0.001; I 2 = 90.6 % ).

Highly significant correlations (Pearson and Spearman’s, p < 0.001; Fig. 1 D) were observed between SF4 values calculated from confluence measurements and cellular viability assays, validating the reproducibility of these high-throughput methods.

All volatile compounds showed highly significant differences among groups ( p < 0.001), indicating a strong impact of processing method on volatile composition.

Overjet exhibited the most pronounced change, with a highly significant reduction from the initial to the final measurements ( p < 0.001), supported by the large number of negative ranks.

A Pearson’s chi-square test revealed a highly significant association between the duration of use and current adoption status (chi-square = 20.10, p ≤ 0.001).

As detailed in the representative scatter and violin plots ( Figure 6 c–h), slope showed a highly significant positive correlation with sugar content (“r = 0.346, p < 0.001”), while aspect exhibited a weak negative correlation trend at the margin of significance (“r = −0.187, p = 0.052”), and canopy layer showed no significant correlation (“r = −0.053, p = 0.584”).

Model 3: Determinants of Business Liquidity (Processing Continuity) FMCS emerges as the dominant and highly significant predictor of business liquidity ( β = 0.336, p < 0.001), indicating that a one-unit increase in financial management competency is associated with a 0.336-point increase in perceived liquidity ( Table 5 ).