Often, the effect of an assumption violation on the sign test result depends on the extent of the violation. Some small violations may have little practical effect on the analysis, while other violations may render the sign test result uselessly incorrect or uninterpretable. In particular, small sample sizes can increase vulnerability to assumption violations. A lack of independence within a sample is often caused by the existence of an implicit factor in the data. If the data are in a particular order, consider the possibility of dependence.
Outliers are anomalous values in the data.
They may be due to recording errors, which may be correctable, or they may be due to the sample not being entirely from the same population. Apparent outliers may also be due to the values being from the same, but skewed or heavy-tailed population. Because the statistic for the sign test is resistant , it will not be substantially affected by the presence of outliers. However, you should remain alert to the possibility that outliers may represent recording errors in the data. If you find outliers in your data that are not due to correctable errors, you may wish to consult a statistician as to how to proceed.