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Laboratory data was compared with ANOVA variance analysis for multiple comparisons where appropriate.
Statistical significance was determined using the unpaired 2-tailed Student's t test or 1-way ANOVA corrected for multiple comparisons where appropriate.
Comparison of means between multiple groups was performed using one-way analysis of variance (ANOVA) with Bonferroni corrections for multiple comparisons where significant differences were found.
Bonferroni correction was used for multiple comparisons where appropriate.
p Values were corrected for multiple comparisons where necessary by the Holm method.
The Bonferroni method was used to correct for multiple comparisons where applicable.
Similar(42)
Comparisons among cell lines were made using analysis of variance (ANOVA) and Tukey-Kramer multiple comparison, where appropriate.
One of the main challenges for such frameworks is the multiple comparisons problem, where the large number of statistical tests performed within a high-dimensional dataset lead to an increased risk of Type I errors (false positives).
All tests were conducted at the 5% level of significance, with no explicit adjustment for multiple comparisons; instead, where appropriate, we present the expected number of false positive findings under the assumption that all null hypotheses are correct, a strongly conservative assumption.
The significance level of the P-value was corrected for multiple comparisons (0.05/k where k equals the number of comparisons).
Statistical analysis was performed by one-way analysis of variance followed by Dunnett multiple comparisons test, where appropriate.
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