“highly significant”

All p -values for the correlation coefficient are highly significant ( p -value < 2.0 × 10 −16 ).

Indeed, the fit of all points rather than of the medians were poor although the (anti-)correlation was highly significant (R 2 = 0.01, p < 2e −16 in both cases).

The one-way ANOVA comparing signal intensity across the different types of hybridization showed a highly significant main effect ( p < 2 × 10 –16 ).

We found that both RM and n sites had a highly significant effect (respectively, F 9,449 = 71, P < 2 × 10 −16 and F 1,449 = 103, P < 2 × 10 −16 ), and the interaction between RM and n sites was also significant ( F 9,449 = 8.99, P < 2 × 10 −16 ).

We found the effect of rurality to be highly significant ( P < 2.0 × 10 -16 ), but it explains only 28% of the overall variation in log positional error magnitude.

A highly significant difference in allelic frequencies was observed across all analyzed polymorphisms (chi-squared test, p < 2 −16 ).

Laminar quantification showed a highly significant difference in Iba1 intensity across cortical layers [ F (4,531) = 90.19, p < 2 × 10 –16 ; one-way ANOVA] with the highest intensity in L1 ( Figure 7E ; L1 vs.

The statistical analysis showed a highly significant evolution of the sugar ( p < 2 × 10 −16 ) and starch ( p < 1.339 × 10 −5 ) curves in function of the reaction time.

Of these 316 genes, 81.5% (256) were found in our original list, a highly significant overlap ( P < 2 × 10 -16 , Fisher's exact test), demonstrating the reproducibility of our methodology.

The enrichment of TEs at species-specific binding sites were highly significant both with a chi-square test (Hs χ 2 = 142.8, Mm χ 2 = 131.4, both p < 2e −16 ) and with permutation tests (n = 10,000, p < 1e −5 ) ( Fig 5A ).

We observed that the A/I ratio was highly significant and associated with improved outcome (A/I ratio: HR = 0.77, P < 2x10 -16 ), while the total TIL score was associated with poorer outcome (TIL HR = 1.03, P = 0.0137).

The effect of time alone was highly significant (P< 2 × 10 -16 , 99.999% CI), though genotype-time interaction was not statistically significant (P = 0.4904, 5% CI) ( Figure 5C ).

Evolutionary niche shift in response to the mean salinity The mean salinity experienced during evolution had a highly significant effect on acclimated tolerance surfaces ( P < 2 × 10 –16 , likelihood‐ratio test—hereafter LRT—between models with vs. without effects of salinity mean).

The ANOVA test revealed that the prediction of fresh weight by the finalized model was highly significant ( p < 2e-16).

The genotype×broomrape race interaction was highly significant for TotAD ( F =3.17, P =2e-16), HeaAD ( F =2.96, P =2e-16), and NecAD ( F =3.63, P <2e-16) ( Supplementary Table S3 ).

Consistent with previous research ( 34 , 35 ), the RSA model predictions closely resembled listeners’ behavior, with the regression of listeners’ actual choice frequencies against model predictions being highly significant at the group level ( r = 0.89, P < 2 × 10 −16 ; Fig. 2A ) and across listeners (logistic regression coefficient = 5.84 ± 0.17, t 40 = 34.24, P < 2 × 10 −16 ).

The smooth terms for longitude and latitude were highly significant, indicating that both geographical coordinates are important predictors of macroplastic concentration ( p < 2e-16, n = 206 for ScriptR results from GAM test, Supplementary Figure S5.2).

However, the overlap between these two very different approaches is highly significant (Fisher's exact test, p < 2 × 10 −16 ), and the functional enrichments they identify are similar (see below).

Phrase duration of female response songs differed highly significant among the five groups of females (ANOVA F 4,71 = 52.7, p = 2 × 10 −16 ).

This leads to a highly significant coefficient ( β ˆ = 4.68 ; p<2e-16).

Effects of genotypes on pre-harvest aflatoxin contamination of maize Aflatoxin contamination was highly significant among maize genotypes (p < 2e-16) ( Table 15 ), with A2207–4 having the lowest level (1.33 μg/kg) and YANGA-5–2 the highest (7.65 μg/kg), as shown in Table 16 .

The analysis of variance (ANOVA) results (Supplementary Data 1 –Table B 3 ) showed a highly significant effect of landmarks on measurement variance ( p -value < 2 × 10 −16 ); however, no significant effect of the operator ( p -value = 1.00) or the interaction between landmark and operator was observed ( p -value = 0.968).

t-PGCS DNMs and likely post-zygotic DNMs) we first fit a simple Poisson regression model that calculated the effect of paternal age on total autosomal DNM counts in the R statistical language (v3.5.1) as follows: glm(autosomal_dnms ~ dad_age, family = poisson(link='identity’)) This model returned a highly significant effect of paternal age on total DNM counts (1.72 DNMs per year of paternal age, p<2e-16), but was agnostic to the family from which each third-generation individual was ‘sampled.’ Importantly, a number of third-generation individuals in the CEPH/Utah cohort share grandparents, and may therefore be considered members of the same family, despite having unique second-generation parents ( Figure 3—figure supplement 1 ).

The highly significant (β = 2.02, z = 45.64, p < 2 × 10 −16 ) intercept, the model estimate of around 8 roadkill, shows a strong baseline effect for predictors at their reference values.

Specifically, adjusting for African ancestry in a multiple regression model largely eliminated the highly significant effect of Black ethnicity on serum creatinine levels (decreased from P<2x10 -16 to a marginally significant P=0.05) whilst the highly significant effect of African ancestry on creatinine remained essentially unchanged when adjusted for Black ethnicity (P<2x10 -16 before and after adjustment).