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Multiple comparison types can be shown on a single map.
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No form of adjustment for multiple comparisons (Type 1 error) were made to the reported data.
After applying Bonferroni correction to adjust for errors of multiple comparisons (type 1 error), no significant deviations remained.
We acknowledge the limited power of our study and the considerable probalility of both type one (due to multiple comparisons) and type two (due to small sample size) errors.
One-way ANOVA: P < 0.01, F = 11.13; post hoc Bonferroni's multiple comparison test: wild-type littermates versus heterozygotes n.s.; wild-type littermates versus homozygotes P < 0.01; heterozygotes versus homozygotes n.s).. Expression levels in the cortex were very low and could not be reliably quantified (not shown).
One-way ANOVA: P < 0.0001; F = 9.948; post hoc Bonferroni's multiple comparison test: wild-type littermates versus heterozygotes non-significant (n.s).; wild-type littermates versus homozygotes P < 0.001; heterozygotes versus homozygotes P < 0.05) (Fig. 5).
To minimize errors due to multiple comparisons, this type of segmentation was not performed.
To take into account the multiple comparisons, the type 1 error rate is specified as 2.5%%.
Finally, the alpha level was not adjusted for multiple comparisons, thus Type I errors cannot be excluded.
To correct for multiple comparisons (inflated Type I error) we applied a false discovery rate (FDR) correction (Benjamini and Hochberg, 1995) and report the adjusted P values.
To correct for multiple comparisons (inflated type I error), I applied a false discovery rate correction (Benjamini and Hochberg, 1995) and report the adjusted p values.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com