What is the parametric test for more than two independent samples?

What is the parametric test for more than two independent samples?

• A. One-factorial ANOVA
• B. Two-way ANOVA
• C. Repeated measures ANOVA
• D. Chi-square

Answer: (A)

When you want to compare the means across more than two independent groups using a parametric approach, you use a one-factorial ANOVA. Here, the grouping variable has several levels (the different independent groups), and the test asks whether the average outcome is the same across all groups or if at least one group differs. It does this by comparing how much of the total variation is due to differences between groups versus variation within groups, yielding an F-statistic. A significant F indicates that not all group means are equal, and you’d typically follow with post hoc tests to identify which specific groups differ. This approach assumes that the outcome is approximately normally distributed within each group, that variances are similar across groups, and that observations are independent. If these assumptions aren’t met, a nonparametric alternative like Kruskal-Wallis can be used, though it tests medians rather than means. The other tests aren’t appropriate here: a two-way ANOVA involves two factors (and possible interactions), repeated measures ANOVA is for related or paired data, and chi-square applies to categorical data rather than comparing group means.

When you want to compare the means across more than two independent groups using a parametric approach, you use a one-factorial ANOVA. Here, the grouping variable has several levels (the different independent groups), and the test asks whether the average outcome is the same across all groups or if at least one group differs. It does this by comparing how much of the total variation is due to differences between groups versus variation within groups, yielding an F-statistic. A significant F indicates that not all group means are equal, and you’d typically follow with post hoc tests to identify which specific groups differ. This approach assumes that the outcome is approximately normally distributed within each group, that variances are similar across groups, and that observations are independent. If these assumptions aren’t met, a nonparametric alternative like Kruskal-Wallis can be used, though it tests medians rather than means. The other tests aren’t appropriate here: a two-way ANOVA involves two factors (and possible interactions), repeated measures ANOVA is for related or paired data, and chi-square applies to categorical data rather than comparing group means.