“highly significant”

Results from Figure 3 showed a highly significant difference in MIC values among the treatments (χ 2 = 270.89, df = 5, p < 2.2 × 10 −16 ), with a very large effect size (ε 2 = 0.85, 95% CI [0.80, 0.89]).

There was, however, a highly significant correlation between change in TE score and GC content (Pearson’s correlation value of −0.1337, p-value<2.2e −16 ) ( Figure 1I ).

The correlation of the concentrations determined by 1 H-NMR and by quantitative methods were highly significant for both the samples of ccDMEM (Pearson: r = 0.99, p = 2.20 * 10 −16 ) and of vcDMEM (Pearson: r = 0.91, p = 2.84 * 10 −6 ).

Comparison between the ratings of the general public and veterinarian responses was highly significant (LR = 330.31; df = 6; p < 2.2e -16 ) with veterinarians rating dog breeds as less sensitive overall than the ratings from the general public.

These results show, that RNAs with diminished abundances in the mutant possess a particularly high amount of cleavage sites, which was also indicated as highly significant by hypergeometric test (enrichment over background distribution of cleavage sites: 6.23-fold; p-value <2.2 −16 ).

Correlations between FPKM-corrected expression levels in same-sex replicate pools were highly significant for both the male ( r 2 = 0.830; Spearman’s ρ = 0.906 ; P < 2.2 × 10 − 16 ) and female ( r 2 = 0.869; Spearman’s ρ = 0.928 ; P < 2.2 × 10 − 16 ) pools.

A Fisher’s exact test confirmed that this difference is highly significant ( P < 2.2 × 10 –16 ), with an odds ratio of 2.76.

We found that the correlation values were highly significant between them all ( Figure 1 , Pearson correlation coefficients of at least 0.95, p -value < 2.2 × 10 −16 ).

This targeting is highly significant as compared to the targeted human proteome counterpart (exact Fisher test, p value <2,2×10 −16 ).

This shift was highly significant as determined by the Mann-Whitney-Wilcoxon Test ( P <2.2*10 −16 ).

This overall deficiency of heterozygotes was statistically highly significant ( t = 43.322, df = 33,234, p < 2.2e-16), and likely due to population subdivision (see results below).

The Wilcoxon–Mann–Whitney test confirmed that these differences were highly significant ( p -value < 2.2 × 10 −16 ). 3.3.

The effect of the fly strain on CCRT was highly significant (Kruskal–Wallis test, p < 2.2 × 10 −16 ).

All correlations were highly significant ( p -value < 2.2 × 10 −16 ), implying that site-specific PTM changes are consistently associated with deviations in protein abundance that cannot be explained by RNA-level changes alone ( Figure 7 b).

Using an Analysis of variance, we found highly significant differences ( p < 2.2 × 10 −16 ) and a non-parametric Wilcoxon Mann–Whitney test confirmed the significant differences of absolute Spearman correlation in healthy versus diseased samples ( p < 2.2 × 10 −16 ).

The P2′ statistic was found to be highly significant across the 57 X substitution lines ( P = 2.2 × 10 −16 ), thus indicating that genotype has an effect on the variation seen for the proportion of progeny sired by the experimental X chromosome male when he is the second to mate.

Similarly, the prevalence of typhoid was notably elevated in Bumbu (91.3%) compared to Bandal (77.4%), with a highly significant X 2 result ( p < 2.2 × 10 −16 ).

Notably, 67% of annotated cancer genes are associated with S100A8/A9 target genes, in comparison to only 55% of noncancer genes, which is highly significant ( P < 2.2 × 10 −16 ; Fisher’s exact test) with contributions from ONG ( P = 2 × 10 −8 ), TSG ( P = 2 × 10 −14 ), and OncoTSG enrichment ( P = 6 × 10 −6 ).

A highly significant statistical difference (X-squared = 1715.4, df = 180, p -value < 2.2 × 10 −16 ) was found between the frequency combinations of the two haplotype sequences. 3.5.

The differences in TSSL and SYL values between the OsSYL3 AA and OsSYL3 CC genotypes were highly significant (Welch’s t ‐test, P = 2.20 × 10 –16 ) (Figure 2d ).

For the 241 significantly differentially accessible regions there was a highly significant enrichment at regions marked as enhancers, compared to ‘All peaks’ ( p < 2.2 x 10 −16 ) or the genome-wide distribution ( p < 2.2 x 10 −16 ) of the chromatin state ( Figure 1d ).

This correlation is also highly significant when averaging polymorphism and divergence within 10-bp adjacent windows ( ρ = 0.50; P < 2.2 × 10 −16 ; fig. 1 ).

The histogram of the distribution of the uncorrected p -values suggested that there was a highly significant excess of low p -values ( p -value < 2.2 × 10 − 16 ; Kolmogorov–Smirnov test; Fig. 2A ).

As expected, we observe an excess of sites with high values of -ln(PIP n ) in real data, whereas the number of sites that show a medium value of -ln(PIP n ) is higher for the simulated data; results are highly significant (Kolmogorov-Smirnov test, p < 2.2e -16 for both datasets).

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.