The sign test is particularly suited to what kind of data design?

The sign test is particularly suited to what kind of data design?

• A. Two related paired samples.
• B. Two independent samples with equal variances.
• C. More than three groups with repeated measures.
• D. Categorical data with three or more categories.

Answer: (A)

The sign test is built around using only the direction of change within pairs, not the size of the change. That makes it ideal for two related (paired) samples—such as measurements on the same subjects under two conditions or at two time points—especially when the difference scores aren’t normally distributed or when the data are ordinal. You simply count how many pairs show a positive difference and how many show a negative difference (ignoring ties), and test whether there's a systematic shift from one condition to the other. This approach works with small samples and requires no assumptions about equal variances or normality. It wouldn’t be appropriate for two independent samples, where you’d compare whole distributions rather than paired differences. It also isn’t suited for more than two related groups (that situation would call for a Friedman test or similar) or for categorical data with three or more categories, where other methods are needed.

The sign test is built around using only the direction of change within pairs, not the size of the change. That makes it ideal for two related (paired) samples—such as measurements on the same subjects under two conditions or at two time points—especially when the difference scores aren’t normally distributed or when the data are ordinal. You simply count how many pairs show a positive difference and how many show a negative difference (ignoring ties), and test whether there's a systematic shift from one condition to the other. This approach works with small samples and requires no assumptions about equal variances or normality.

It wouldn’t be appropriate for two independent samples, where you’d compare whole distributions rather than paired differences. It also isn’t suited for more than two related groups (that situation would call for a Friedman test or similar) or for categorical data with three or more categories, where other methods are needed.