Analytical data were evaluated by one-way ANOVA (P < 0.005) followed by the Tukey's test for multiple comparison procedures.
In this paper, we consider nonparametric multiple comparison procedures for unbalanced two-way factorial designs under a pure nonparametric framework.
In this paper, the design-oriented two-stage and data-analysis one-stage multiple comparison procedures for successive comparisons of exponential location parameters under heteroscedasticity are proposed.
Next, the general problem that is associated with most multiple comparison procedures; relatively low statistical power, is highlighted and new step-wise correction procedures that are designed to overcome this problem of low power are introduced.
On the two datasets, results depicted by Receiver Operating Characteristic (ROC) curves together with Multiple Comparison Procedures (MCP) show that QDA has in general the best behavior as classification algorithm.
Traditional dose ranging trials focus on pairwise comparisons between placebo and each investigational dose, while in recent years MCP-Mod (Multiple Comparison Procedures & Modeling) arose and gained popularity in the design and analysis of dose ranging trials.
In order to assess differences statistically, multiple comparison procedures are applied.
Open field cumulative counts were compared using 2-way repeated measure ANOVA (genotype vs. time) with pair-wise multiple comparison procedures using Holm-Sidak method.
Student t-test or two ways Analysis of Variance (ANOVA) followed by Post-Hoc pairwise multiple comparison procedures (Bonferroni t-test) were performed.
Pairwise multiple comparison procedures (Scheffé's and Holm-Sidak post-hoc methods for electrophysiology and immunohistochemistry, respectively) were applied to more accurately assess the source of variation between groups.
In the case of a global significant age effect, in order to isolate the group or groups that differ from the other, we have realized multiple comparison procedures using the Dunn's Method.
