MCQs Comparing Dependent (Paired) vs. Independent Samples t-Test

What is the primary difference in data structure between a paired t-test and an independent t-test?

• Paired tests compare means from the same group at different times, while independent tests compare two separate groups.

• Independent tests require equal sample sizes, while paired tests do not.

• Paired tests assume unequal variances, while independent tests assume equal variances.

• Independent tests are only used for proportions.

Which test is more powerful (likely to detect a true effect) when comparing two means?

• Independent t-test, because it uses larger sample sizes.

• Paired t-test, because it controls for individual variability by matching data points.

• Both tests have equal power.

• z-test, because it assumes known population parameters.

How do degrees of freedom (d.f.) differ between the two tests?

• They are always the same for both tests.

• They depend on the sample sizes and test type.

• They are only relevant for one of the tests.

• Degrees of freedom do not affect test results.

Which assumption is unique to the independent t-test but NOT required for the paired t-test?

• Normality of differences between pairs.

• Homogeneity of variances (equal variances between groups).

• Independence of observations within each group.

• Measurement on an interval/ratio scale.

When analyzing pre-test/post-test scores of students, which test is appropriate?

• Independent t-test, because the groups are separate.

• Paired t-test, because the same students are measured twice.

• Chi-square test, because the data is categorical.

• ANOVA, because there are multiple groups.