“highly significant”

This test revealed a highly significant difference in the overall distribution ( X-squared = 81.204,df = 4, P -value < 2.2e-16 ).

The overlap between CNM genes and genes correlated with aging was highly significant (p < 2.2 × 10 –16 , Fisher’s exact test).

Wilcoxon rank sum test with continuity correction revealed highly significant changes for old versus young cortex and hippocampus circRNA TPM values ( P < 2.2 E-16) ( Fig. 2d ).

These tests were all found to be statistically highly significant, with p < 2.2 × 10 −16 .

The Pearson correlation coefficients of uniquely mapped reads between biological replicates are from 0.961 to 0.994, which are highly significant ( p -values < 2.2e−16).

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 ).

Interestingly, pairwise comparisons of the data from each analysis yielded highly significant positive correlations (Pearson’s test, P < 2.2 × 10 −16 ) ( Figure 3 ; Supplementary Table 3 , available online).

DNA methylation at 84.7% of these sites was significantly associated with at least one common genetic variant using a stringent mQTL threshold (P < 1x10 -8 ) ( S2 Table ); this represented a highly significant enrichment for mQTL effects (P < 2.2x10 -16 ) compared to less-heritable DNA methylation sites (defined as those with A < 0.8), amongst which only 24.5% were associated with a mQTL variant.

The overestimates were highly significant (1-tailed paired t -test, t = 26.60, df = 519, p < 2.2 −16 ).

A MANOVA analysis revealed highly significant differences among genera with respect to biotemperature, precipitation, and evapotranspiration values associated with their records ( p = 2.2 × 10 −16 ; DF = 9; F = 20.617; Pillai’s Trace = 0.9114).

Altogether, these results suggest that moonlighting proteins are prone to be involved in human diseases as it is pointed by it respective ODD ratios; 16.47 (CI 95% 10.95–25.44) in the OMIM analysis being highly significant (Fisher’s exact test value; p < 2.2e-16) (See S6 Table).

Conversely, a robust positive correlation was observed between PKMYT1 expression and TMB, with a correlation coefficient of R=0.4 and a highly significant P value ( Figure 5C , P<2.2e−16).

The high proportion of variation among environmental means in Figure 3 is supported by highly significant (p< 2.2e-16) differences among the mean effect of each environment.

3C ), with the Kolmogorov-Smirnov test revealing a highly significant difference in the two distributions (p = 2.2×10 −16 ).

A chi-squared test revealed a highly significant difference in the distribution of genes across score ranges among the three categories (χ2 = 519.5, df = 18, p < 2.2 × 10−16), indicating that gene distributions differed substantially between them.

Strikingly, PFC Aliceblue and NAc Cadetblue 2 showed a highly significant overlap in their gene content, with 72 overlapping genes ( S2 Table ; p = 2.2 x 10 −16 ).

The median Laa value for SCT strain-specific genes (183 aa) was smaller than that for the other genes (280 aa), the difference being highly significant according to a Wilcoxon rank sum test ( p -value < 2.2e-16).

A linear statistical model for the rate constant of photoinhibition ( k PI ) was highly significant ( F (8,95) = 50.3, P < 2.2 × 10 −16 ) and showed that k PI was significantly affected by light color (coefficient 0.70 ± 0.0095, P = 6.75 × 10 −11 ) and leaf group (coefficient 0.016 ± 0.0069, P = 0.0256) but not by the other variables (see Summary of statistics available as Supplementary data at Tree Physiology Online).

There was a highly significant correlation ( r = 0.876, P -value <2.2 × 10 −16 ) ( Supplemental Fig.

The overall percentage of HG disease was highly significant, with a hazard ratio of 4.45 for the interquartile range (95% confidence interval 3.30–6.01, P < 2.2 × 10 −16 ), and was similar to the percentage of HG disease seen in the worst core.

The difference in half‐life distribution is highly significant with a p ‐value = 2.2 × 10 −16 (Kolmogorov–Smirnov test).

The sequencing data showed highly significant expression correlation with the array data ( R = 0.88, p < 2.2 × 10 −16 ) for 101 genes whose expression was significantly altered in KO mouse (Table S3 and Figure S8 ).

There exist only small differences in their declared residence area (zip code), but they are older (median age 35 against 24), more likely men (men proportion 62.9% against 49.9%) and live within the Lyon-Villeurbanne urban area (85.3% against 81.7%, all theses differences are highly significant, p-value <2.2*10 −16 ).

The correlation coefficients were highly significant in each case (PrEC: r = 0.80, p < 2.2e−16; LNCaP: r = 0.91, p < 2.2e−16; NPF: r = 0.84, p < 2.2e−16; CAF: r = 0.95, p < 2.2e−16), but not completely concordant with WGBS data (Additional file 5 : Figure S4A).

Comparison using a paired t-test for R s from the predictive model and their corresponding permutated variant indicated a highly significant difference between both model types ( p -value < 2.2 × 10 −16 across cases), indicating that these model predictions are extremely unlikely to be due to chance.