Sentence examples for mean comparison test from inspiring English sources

Tukey's post hoc multiple mean comparison test was used to test for significant differences between treatments (at 5% level).

Tukey's post hoc multiple mean comparison test was used to test for significant differences between treatments (at p ≤ 0.05%).

A sample mean comparison test was used to indicate the normal CPT, useful to get an arbitrary quantitative definition of cutaneous IA.

The data obtained for each of the evaluated quantitative variables were subjected to an ANOVA analysis and a mean comparison test after an analysis of normality of the data.

With regard to two-group comparison statistics, we applied to all data a univariate mean comparison test that was either parametric or non-parametric depending on the normality of the data.

As shown in Fig. 1B, individuals who carry two copies of the short allele of the 5-HTTLPR polymorphism invest $2.69 (about 28% of the average risky allocation) less in the risky asset than those carrying one or two copies of the long allele of the genotype (p<0.02 in a one-tailed mean comparison test), in excess of the benchmark model.

Unlike otherwise stated, non-parametric Wilcoxon (comparison of two means) or ANOVA (comparison of more than two means) mean comparison tests were conducted with significance and high significance thresholds respectively set at p < 0.05 and p < 0.01.

Monovariate mean comparison tests and multivariate Principal Components Analysis (PCA) or Partial Least Squares-Discriminant analysis (PLS-DA) were carried out with the open source software R35 with the RVAidememoire, mixOmics and ade4 packages (downloaded on march 2016).

Mean comparison tests (t tests) were conducted by omitting one cluster dummy variable at a time and evaluating the significance of the coefficients on the other dummy variables by examining the t-statistics associated with each estimated coefficient.

Second, their analysis used mean comparison tests to compare usage on the basis of five farm and farmer demographic drivers, while we use multivariate analysis (logit) to analyze usage with 16 drivers, including farm, farmer demographic, and regional variables.

For all data, equal variance test and normality tests were performed prior to other mean comparison testing.