Sentence examples for multiple sample comparisons from inspiring English sources

Statistical significance was determined by one-tailed t test assuming unequal variance for two sample comparison and by one-way analysis of variance (ANOVA) followed by Tukey's honestly significant difference (HSD) test for multiple sample comparisons.

Normalized signals were used for class comparisons of variance by two-way t-tests for two sample comparisons and parametric univariate F-tests for multiple sample comparisons (p<0.001, FDRs<2%; BRB-ArrayTools, (http://linus.nci.nih.gov/BRB-ArrayTools.html) to identify significantly differentially expressed genes.

This is especially useful for multiple sample comparisons; (ii) Gene classification was based on multiple comparison.

One-way analysis of variance and a Tukey or Dunnett's posttest was performed for multiple sample comparisons.

The statistical significance of the gene expression was calculated using a one-way analysis of variance (ANOVA) for multiple sample comparisons (SAS v9.4 software).

Statistical analysis was done using one tailed students t-test for pair wise comparison or ANOVA followed by post-hoc Tukey test for multiple sample comparisons.

Multiple samples comparisons were performed using one-way ANOVA (parametric) or Kruskal-Wallis one-way analysis of variance on ranks (non- parametric).

ANOVAs and multiple-samples comparisons were performed with the Statgraphics® software.

Multiple-samples comparisons were performed between genotypes for each treatment independently.

Multiple-samples comparisons were carried out using the Fisher test (Least Significant Difference) at the 5% threshold.

Such a log rank test follows a χ2 distribution in the case of multiple-sample comparison; in the case of two-sample comparisons, it computes a Z score referred to a normal distribution.