“highly significant”

A highly significant association was observed between the type of sport and anatomical site of injury (χ2 = 712.59, P < 0.001).

Implementing this analysis in a searchlight revealed a significant cluster in our lOFC ROI ([ x , y , z ] = [−45,31,−15], t [29] = 3.90, pTFCE = 0.015; Fig 4C ), showing that even a cluster-agnostic analysis resulted in a highly significant overlap between voxels coding identity-specific expectations and those predicting choice (DICE coefficient = 0.559, p < 0.001 permutation test).

Claiming the difference between these two groups is highly significant at p < 0.001.

In combination with Se, this increase depicted 65.7 ± 13.1% and remained highly significant ( P <0.001).

Statistical analysis demonstrated a highly significant difference in sO 2 (P<0.001).

The association between internal opening status and treatment group was highly significant (p < 0.001).

From the statistical significance tests for model comparison, the Bootstrap Likelihood Ratio Test (BLRT) was highly significant ( p < 0.001) for all models, supporting the inclusion of each latent class.

Among these, PS exhibited the strongest discriminative performance, with highly significant differences between csPCa and ciPCa in the overall, PZ, TZ, and ≥1 cm lesions (all P<0.001).

ERD demonstrated pervasive and highly significant positive associations with pain across multiple regions, with the largest effects observed in the neck (β = 0.0071, 95% CI: 0.0055–0.0087, p < 0.001), arm (β = 0.0069, 95% CI: 0.0054–0.0084, p < 0.001), back (β = 0.0063, 95% CI: 0.0049–0.0078, p < 0.001), and chest (β = 0.0057, 95% CI: 0.0038–0.0076, p < 0.001).

More quantitatively, NleC unfolded at 13.5 ± 0.5 pN (mean ± SEM; N = 64 at 100 nm/s) while protealysin unfolded at 50.7 ± 2.7 pN (mean ± SEM; N = 37 at 100 nm/s), a highly significant difference ( p < 0.001; unpaired T ‐test). 2.3 Effector NleC is mechanically compliant Not only were the magnitudes of the unfolding forces between NleC and protealysin distinctly different, the slopes of the two dynamic force spectrums were as well (Figure 1f ).

Global indicators confirmed the factorability of the matrix, highlighting an optimal Kaiser–Meyer–Olkin coefficient (KMO = 0.85) and a highly significant Bartlett's test of sphericity ( X 2 = 3550.66, p < .001).

The day x farm interaction was highly significant (all contrasts p < 0.001), indicating that age-related inactivity trajectories differed between farms.

The analysis revealed a highly significant effect of treatment on seasonal disease pressure (p < 0.001), whereas the effect of year was not significant (Table 3 ).

The interaction between year and site was highly significant ( P < 0.001), reflecting a much stronger interannual increase in fruit removal at Campo than at Mbalmayo (Fig. 4 ).

Kawasaki patients showed a much higher prevalence of classic mucocutaneous manifestations such as conjunctivitis (96.2%), stomatitis (96.2%), lymphadenitis (96.2%), hand and foot edema (57.7%), and maculopapular rash (80.8%) compared to MIS-C, with all differences being highly significant ( p <.001).

The omnibus test was highly significant, χ2(6) = 423.50, p < 0.001, with Kendall’s W = 0.26 indicating a small to moderate level of agreement in the rank ordering across respondents.

The difference between these two trends was highly significant ( P < 0.001).

The analysis revealed a highly significant group × time interaction ( F [1, 41] = 208.58, p < 0.001), indicating that the recovery trajectory of the IMT group was significantly superior to that of the control group.

The direct effect of PSP on RS was highly significant for females (Effect = 0.123, p <0.001) and marginally significant for males (Effect = 0.0538, p = 0.0505), and OB significantly predicted RS only in the female group.

Paired t -test analysis demonstrated a highly significant difference between the two groups ( p < 0.001; Cohen’s dz = 1.62).

A one-way ANOVA across the seven methods at ε = 2 cm confirmed that the differences among methods were highly significant (F = 8.9 × 10 4 , p < 10 −3 ), and the EB-HC-3D advantage over EB-HC corresponded to a very large effect size (Cohen’s d = 7.16).

Plant height The overall linear mixed-effects model (LMM) for the spring (Experiment 1) revealed a highly significant main effect of time on plant height (F(8, 360) = 523.91, p < 0.001), whereas the main effect of treatment was marginally non-significant (F(4, 45) = 2.37, p = 0.07).

p < 0.01→ highly significant. p < 0.001→ very highly significant. p > 0.05 → fail to reject the null hypothesis (not statistically significant—the difference could just be due to random variation).

Two-way ANOVA showed that the main effects of elevation on MBC, MBN, MBP, DOM, MNC, POC, and MAOC were not significant ( p > 0.05), whereas both cover treatment and the elevation × cover treatment interaction had highly significant effects ( p < 0.001).

The differences among these four groups were highly significant (one-way analysis of variance F = 18.73, p < 0.001, and eta-squared = 0.77; the Tukey HSD post hoc results are shown in Table 2 and Figure 3 B).