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Multiple comparisons were controlled by the Bonferroni method where appropriate.
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As we tested our hypothesis for seven different chronic physical conditions, the significance level for multiple comparisons was controlled by applying the Bonferroni correction for multiple comparisons and p ≤ 0.007 was considered statistically significant.
The amplitude of the comparison was controlled by a staircase method that tracked the point of subjective equality, i.e., the amplification factor for the comparison stimulus that rendered it as intense as the standard.
Drug effects were controlled by comparison with injections of saline.
Nevertheless, when conducting between-groups comparison, the outcome measures at baseline were controlled by including them as independent variables in the multiple linear regression models.
Multiple comparison error was controlled by a false discovery rate (FDR) transformation [ 93].
Comparison showed that the flux is controlled by both bulk diffusion and surface exchange, even for such thick membranes, and that the apparent kδ varies significantly from one experiment to another.
Corrections for multiple comparisons were performed by controlling the false discovery rate at 5% (Benjamini and Hochberg 1995) with the function p.adjust in R v3.0.3 (R Core Team 2014).
Confounding variables were all symmetrically distributed between comparison groups, so they were not controlled by performing analysis of covariance.
Post-hoc paired group comparisons were performed by Scheffe testing to control the overall type I error rate.
Case-control comparisons were analyzed separately by series since data on nutrient and antioxidant index intake was collected by different methods between study series.
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