In a design with one independent variable having two groups, which analysis compares group means?

In a design with one independent variable having two groups, which analysis compares group means?

• A. T-Test
• B. ANOVA
• C. Chi-Square
• D. Friedman's ANOVA

Answer: (A)

When you have one independent variable with two groups, the goal is to determine whether the average outcome differs between those two groups. The standard method for this is the t-test, which compares the two group means while taking into account the variability of scores within each group. It calculates a t-statistic from the difference between the means divided by the estimated standard error, and then uses that to assess whether the observed difference is likely due to chance. If the result is statistically significant, you conclude that the groups differ on the average outcome. ANOVA, by contrast, is designed to compare three or more group means at once; with only two groups, it reduces to the same comparison as the t-test but is more commonly used when there are three or more groups or additional factors involved. Chi-square tests are for relationships between categorical variables, not comparing means. Friedman's ANOVA is a nonparametric alternative for comparing related (repeated) measures across more than two conditions. If the data don’t meet t-test assumptions, nonparametric options like the Mann-Whitney U test (two independent groups) can be used.

When you have one independent variable with two groups, the goal is to determine whether the average outcome differs between those two groups. The standard method for this is the t-test, which compares the two group means while taking into account the variability of scores within each group. It calculates a t-statistic from the difference between the means divided by the estimated standard error, and then uses that to assess whether the observed difference is likely due to chance. If the result is statistically significant, you conclude that the groups differ on the average outcome.

ANOVA, by contrast, is designed to compare three or more group means at once; with only two groups, it reduces to the same comparison as the t-test but is more commonly used when there are three or more groups or additional factors involved. Chi-square tests are for relationships between categorical variables, not comparing means. Friedman's ANOVA is a nonparametric alternative for comparing related (repeated) measures across more than two conditions. If the data don’t meet t-test assumptions, nonparametric options like the Mann-Whitney U test (two independent groups) can be used.