Sentence examples for correlative bias from inspiring English sources

This minimized any bias that resulted from lesions that regressed between screening and follow-up [37] and the correlative bias between VIA and colposcopy which rely on similar visual clues [13], [38], which could have led to an overestimation of sensitivity in other studies.

Our sample had resistance training-induced gains in strength, LBM and muscle fibre CSA that represented a substantial range and that were normally distributed and therefore less prone to correlative bias due to outlying data points.

Again, this correlative trend is wholly dependent on existing codon bias (for d N/d S without codon bias, r = 0.029, P = 0.328; fig. 5 C and for d N without bias r = −0.038, P = 0.197; fig. 5 D).

Although we have emphasized positive- versus negative-strand cross-correlation and the fragment-length estimation problem, our approach to eliminating mappability bias is relevant to other correlative-type analysis of short-read data.

Like all species distribution modeling exercises, our results are correlative and there were inherent sources of bias at each step of the modeling process.

Correlative analyses were performed using Pearson's correlation.

Non-parametric correlative analyses were performed using the Spearman correlation.

On the other hand, local correlation analysis allowed us to identify small correlative areas mostly in non-correlative spaces.

On the other hand, the reviewers also feel that the evidence for competition is correlative and weak at this point, and the mechanisms by which Hevin bias thalamocortical vs. intracortical synapse formation is unclear.

Using machine learning to identify correlative patterns in data is an extremely powerful approach to understanding both the nuances and biases of our data and the unexpected very real patterns that our current theoretical understandings failed to point us towards.

In contrast to the currently prevailing method of estimating species distributions using expert-drawn range maps, correlative species distribution models (SDMs) can provide estimates at very fine spatial grains and largely account for widespread sample bias as well as the prevalent Wallacean shortfall in species occurrence data.