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Once all the p-values have been computed, we set a significance threshold (p^) and validate all the pairs of nodes that are associated with a p-value smaller than the threshold (p^).
A generalized family-wise error rate (gFWER) model is used to set a significance level adjusted for multiple testing.
One critical difference between the two genome-wide approaches is that Wray et al. [9] set a significance threshold for the loci selected for disease prediction, whereas Meuwissen et al. [10] use all loci regardless of whether they affect or not the trait considered.
For AROR comparisons, we only considered the 1,232 RefSeq transcripts covered by the chromosome 19 & 20 regions on the NimbleGen tiling array, and set a significance cutoff of E< = 5, corresponding to the E-value chosen for the full genome, and also occurring at the minimum achievable False Discovery Rate (FDR) of 0.1.
Since primary analysis includes two group comparisons, we set a significance level at 0.025.
All tests were two-tailed, and we set a significance level of 0.05.
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Looking at the standardized residuals of each cell and setting a significance of p < .01, (z = ± 2.58), it was found that low users were significantly more likely to interact with kudos (z = 4.26), and less with university notices (z = −3.48), suggesting that they were most interested in celebrating their peers and least responsive to university information.
We analyzed the differences between the mean scores setting a significance level of 95%.
By setting a significance threshold, only variants in linkage disequilibrium (LD) with undiscovered mutations of moderate effect size are effectively considered in the enrichment or depletion analysis.
We will set a liberal significance level (p<0.10) to select variables that remain in the model.
We used two-sided tests to compare means and BTL attrition rates, and set a statistical significance threshold of p = 0.05.
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