# Two-Related-Samples Test Types

The tests in this section compare the distributions of two related variables. The appropriate test to use depends on the type of data.

If your data are continuous, use the sign test or the
Wilcoxon signed-rank test. The **sign
test** computes the differences between the two variables
for all cases and classifies the differences as positive, negative,
or tied. If the two variables are similarly distributed, the number
of positive and negative differences will not differ significantly.
The **Wilcoxon signed-rank test** considers information about both the sign of the differences and
the magnitude of the differences between pairs. Because the Wilcoxon
signed-rank test incorporates more information about the data, it
is more powerful than the sign test.

If your data are binary, use the **McNemar test**. This test is typically
used in a repeated measures situation, in which each subject's response
is elicited twice, once before and once after a specified event occurs.
The McNemar test determines whether the initial response rate (before
the event) equals the final response rate (after the event). This
test is useful for detecting changes in responses due to experimental
intervention in before-and-after designs.

If your data are categorical, use the **marginal homogeneity test**. This test
is an extension of the McNemar test from binary response to multinomial
response. It tests for changes in response (using the chi-square distribution)
and is useful for detecting response changes due to experimental intervention
in before-and-after designs. The marginal homogeneity test is available
only if you have installed Exact Tests.