Barely Significant
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“highly significant”

132,143 sentences · 132,143 papers · 160,126 search hits before verification · confirmed specimen

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p=0.09

Listed by Hankins (2013)

In the literature

highly significantp < 0.0010.0× alphaqualifiedgold
This difference was highly significant (p < 0.001). 4 Discussion The goal in obtaining these molecules is not only to maximize their extraction but also to ensure that they retain their activities for future application and use.
Optimizing protein yield from growing deer antlers.
Front Bioeng Biotechnol · 2025 · PMC12245863
highly significantP <.0010.0× alphaqualifiedgold
Results The intervention showed highly significant effects across all domains of the primary outcome measure ( P <.001), with the most notable effects on mental health ( F 2, 118 =425.88; η 2 =0.88), energy and vitality ( F 2, 118 =371.73; η 2 =0.86), and role limitations—emotional ( F 2, 118 =335.45; η 2 =0.85).
highly significantp < 0.0010.0× alphaqualifiedgold
The solid yellow line represents the moving average over the elapsed MU to facilitate the visualization of trends in the parameters. Penumbra: In the longitudinal orientation (Figure 3a,b ), both sides of the penumbra showed a significant decreasing trend that was particularly pronounced in the negative side and exhibited a highly significant decrease over time ( p < 0.001), as shown in Table 1 .
highly significantp<0.0010.0× alphaqualifiedgold
The model also exhibited a strong overall fit, with a pseudo-R2 of 0.6745 and a highly significant likelihood ratio χ2 (χ2=638.39, df=2, p<0.001), explaining a substantial proportion of the variation in VLS among CALHIV.
highly significantp ≤ 0.0010.0× alphaqualifiedgold
This performance difference remained highly significant in the randomized k-fold evaluation, where significant differences were again observed for both the low [χ 2 (3) = 135.05, p ≤ 0.001) and high [χ 2 (3) = 162.84, p ≤ 0.001] class-separability contrasts. 4 Discussion The objective of the current study was to investigate the extent to which pBCI model evaluation metrics may be biased when temporal dependencies between train and test samples are not considered in cross-validation.