In a study comparing two first-grade classes, which statistical procedure is appropriate for comparing their mean oral reading fluency scores?

In a study comparing two first-grade classes, which statistical procedure is appropriate for comparing their mean oral reading fluency scores?

• A. Analysis of Variance
• B. Pearson correlation
• C. T-test
• D. Chi-square test

Answer: (C)

When you want to know if two independent groups differ on a numeric outcome, compare their average scores with an independent-samples t-test. Oral reading fluency scores are continuous data, and you’re looking to see if the mean score in one first-grade class is different from the mean in the other class. The t-test specifically assesses whether the observed difference in means is likely to reflect a real difference in the populations or just random variation, assuming the data are approximately normally distributed within each group and the variances are not wildly unequal. Pearson correlation would measure how two continuous variables relate to each other, not whether groups differ in their means. The chi-square test is for counts in categories, not for comparing average scores on a numeric measure. Analysis of variance is used to compare means across three or more groups; with just two groups, the t-test is the direct, standard method (and is mathematically equivalent in approach to a two-group ANOVA).

When you want to know if two independent groups differ on a numeric outcome, compare their average scores with an independent-samples t-test. Oral reading fluency scores are continuous data, and you’re looking to see if the mean score in one first-grade class is different from the mean in the other class. The t-test specifically assesses whether the observed difference in means is likely to reflect a real difference in the populations or just random variation, assuming the data are approximately normally distributed within each group and the variances are not wildly unequal.

Pearson correlation would measure how two continuous variables relate to each other, not whether groups differ in their means. The chi-square test is for counts in categories, not for comparing average scores on a numeric measure. Analysis of variance is used to compare means across three or more groups; with just two groups, the t-test is the direct, standard method (and is mathematically equivalent in approach to a two-group ANOVA).