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

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

• A. Repeated measures ANOVA
• B. One-way ANOVA
• C. Paired t-test
• D. Pearson's r

Answer: (A)

When you have three or more related measurements from the same participants, you need a test that accounts for the fact that the samples are not independent. Repeated measures ANOVA does exactly that by treating each participant’s responses across conditions as related observations and testing whether there are overall differences across the conditions. It’s the parametric extension of the idea behind the paired t-test, which only handles two related samples. Repeated measures ANOVA assumes the dependent variable is roughly normally distributed within each condition and that the differences between conditions are fairly similar in variance (sphericity). If sphericity isn’t met, degrees of freedom are adjusted with corrections like Greenhouse-Geisser or Huynh-Feldt. If the data don’t meet parametric assumptions, a nonparametric alternative is the Friedman test. The other options don’t fit because they either compare independent groups, or involve correlations between two variables rather than comparing means across multiple related conditions.

When you have three or more related measurements from the same participants, you need a test that accounts for the fact that the samples are not independent. Repeated measures ANOVA does exactly that by treating each participant’s responses across conditions as related observations and testing whether there are overall differences across the conditions. It’s the parametric extension of the idea behind the paired t-test, which only handles two related samples. Repeated measures ANOVA assumes the dependent variable is roughly normally distributed within each condition and that the differences between conditions are fairly similar in variance (sphericity). If sphericity isn’t met, degrees of freedom are adjusted with corrections like Greenhouse-Geisser or Huynh-Feldt. If the data don’t meet parametric assumptions, a nonparametric alternative is the Friedman test. The other options don’t fit because they either compare independent groups, or involve correlations between two variables rather than comparing means across multiple related conditions.