“highly significant”

The model revealed highly significant main effects of total body length (log 10 TBL) ( F = 3335.41, p < 2.2e‐16), indicating that mandible length strongly and predictably scales with overall body size.

All reported correlations were highly significant ( p < 2.2 × 10 −16 ).

Given that we observe 117 mutations in hypomethylated CGIs, but only expect 50.0 (117*(1/2.34*)) with the genome-wide autosomal mutation rate, this represents a highly significant enrichment (Poisson test, p = 2.2 × 10 −16 ).

The variance of differences for the genomic GC proportion in humans, mice, and fruit flies respectively is σ ^ n 2 = ( 0.000673 , 0.00055 , 0.00103 ) compared to σ ^ r 2 = ( 0.00469 , 0.00552 , 0.0021 ) for the permuted genome sequence, with the ratios being highly significant in all cases (F-test with p-value p < 2.2 e - 16 ).

Finally, we observed a highly significant increase (one-sided Student’s t -test, p = 2.2 × 10 −16 ) in average epistasis values in the winning CR genotypes compared with the overall population, indicating that positive epistasis plays an important role in determining the fittest genotypes in this selection (Fig. 5b ).

It is evident from the data that genome size influences the frequency of microsatellites, supported by a highly significant positive correlation between genome sizes and frequency of microsatellites ( R 2 = 0.53 and P -value = 2.2e –16 ) ( Fig. 2D ).

The overall distribution of average within-pair DNA methylation differences showed a highly significant skew to the right in ASD-discordant twins ( P <2.2e−16, Kolmogorov–Smirnov test), with a higher number of CpG sites demonstrating a larger average difference in DNA methylation.

This revealed a highly significant association between gene age and expression specificity in both species ( D. melanogaster : χ 2 = 1092.66, df = 5, p < 2.2 × 10 −16 ; A. aegypti : χ 2 = 890.74, df = 5, p < 2.2 × 10 −16 ), indicating that stage‐specific genes are significantly enriched for evolutionarily young genes. 2.4.

Using a pairwise comparison of 5286 genes with adjP ≤ 0.1 for log fold change of WT MR versus WT Con and NRF2 KO MR versus NRF2 KO Con, the correlation of the effect of MR in the two genotypes was highly significant (R 2 = 0.64, p < 2.2 × 10 −16 , Supplementary Figure S2 ).

We report a highly significant positive correlation between the exon length and number of homologous exons (Figure 4 , Kendall’s τ = 0.133, p-value < 2.2e-16) suggesting that isolate/species-specific genes tend to be shorter.

However, it is interesting to note the highly significant co-directionality (binomial test: p < 2.2e−16) of the expression changes observed for nitrogen-limited cultures and in hospite symbionts, with 92.67% of the 12,295 (11,394) DEGs changing expression in the same direction (Fig. 2 e).

Based on paired t -test, we showed that the exponential regression model had highly significant mean R 2 accuracy than each of the existing methods (Holsteins: t = 584.8–37281; p < 2.2–16; Jerseys: 1178.5–5861.4; p < 2.2e-16).

When examining 92 age‐associated metabolites, the correlation between the age beta coefficient was highly significant ( r = 0.79, p = 2.2e‐16; Figure S2B ), suggesting the reliability of our method. 91 of them changed with age in the same direction, among which 27 were statistically significant.

This revealed a highly significant agreement of the number of events between paired replicates (Supplementary Fig. 2b , Pearson's correlation 0.997, n = 30, two-sided, p value < 2.2e−16), indicating that uvCLAP events capture the amount of pulled down RNA exceptionally well.

There is a highly significant overlap in the promoters that gain hmC at 12 hrs and those that lose mC at 72 hrs ( P value < 2.2×10 −16 , Fig. 1d , Supplementary Fig. 6 ).

To further quantify these differences, we applied a two-sided Wilcoxon signed-rank test between the Mean and SD, which demonstrated a highly significant difference ( p -value < 2.2e-16, N = 12,321 compound-compound interactions).

Comparing the two sets of genes, we observed a highly significant overlap of 193 genes whose expression was altered by either IL-1β treatment and IL-1R8 deficiency (Fischer’s exact test; P value = 2.2e-16; Pearson correlation coefficient = 0.95; 61.7% downregulated and 38.3% upregulated, Supplementary file 3 ).

Subsequent statistical analysis revealed a highly significant ( p < 2.2 × 10 −16 ) cell cluster composition between the baseline sample NI Day + 0 ( n = 217 cells) compared to differentiated samples S Day + 15 ( n = 577 cells) and SON Day + 15 ( n = 400 cells) as well as between the differentiated samples ( Figure 3 C, Table S5 ).

RNA-seq and CAGE of CdLS neurons showed highly significant overlap (Odds ratio = 11.86, P < 2.2e-16).

The investigation into the relationship between the effect sizes of ASVs and their respective taxonomic groups revealed highly significant results (Kruskal–Wallis test, P − value < 2.2 × 10 − 16 ) for both N conditions on all three days, suggesting a disproportionate enrichment of large effect ASVs within certain taxonomic groups.

Note that likelihood ratio tests comparing the model results without and with the T1WP correction were all highly significant ( p < 2.2 × 10 − 16 ), probably due to the high number of observations and relatively small number of terms in the model.

This difference is highly significant ( P = 2.21 × 10 −16 ), considering that the mean ARE score for human mRNA transcriptome is 3.8 ( Spasic et al. 2012 ).

Our second question: Did adaptation to the media or the presence of SA alter phage resistance? Our analysis revealed highly significant effects of both Treatment ( F = 132.73, df = 3, P < 2.22e-16) and Condition ( F = 41.83, df = 4, P < 2.22e-16) on the area under the growth curve, as well as significant Treatment × Condition interaction ( F = 37.66, df = 12, P < 2.22e-16).

Pagel’s λ test indicated a very strong and highly significant phylogenetic signal (λ=0.99, P <2.22e-16), indicating that attC abundance is strongly structured by evolutionary history.

The spatial autoregressive parameter had a positive value and was highly significant ( p < 2.22 × 10 −16 ), indicating that endemism in a given area tends to increase with increasing endemism in surrounding grid cells (independent of the other parameters in the model).