The Sign test is a nonparametric alternative to the t-test for dependent samples. The test is applicable to situations when the researcher has two measures (e.g., under two conditions) for each subject and wants to establish that the two measurements (or conditions) are different. Select Comparing two dependent samples (variables) from the Nonparametric Statistics Startup Panel - Quick tab to display the Comparing two variables dialog box, from which you select variables from two lists. Each variable in the first list will be compared to each variable in the second list.

Assumptions and interpretation. The only assumption required by this test is that the underlying distribution of the variable of interest is continuous; no assumptions about the nature or shape of the underlying distribution are required. The test simply computes the number of times (across subjects) that the value of the first variable (A) is larger than that of the second variable (B). Under the null hypothesis (stating that the two variables are not different from each other) we expect this to be the case about 50% of the time. Based on the binomial distribution we can compute a z value for the observed number of cases where A > B, and compute the associated tail probability for that z value. For small n's (less than 20) you may prefer to use the tabulated values found in Siegel and Castellan (1988) to evaluate statistical significance.

See Comparing Two Variables - Quick tab for further details.