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The signed rank test can be used to make inferences about the median of an unknown distribution function F(x) under the assumption that the probability density (mass) function is symmetric. If the distribution is symmetric, then the median of the distribution is equal to the mean of the distribution, and they can both be denoted by μ = F −1 (0.5). The sign test can also be used to provide inferences about the median without the necessity of making any assumptions about the distribution function F(x). However Darnell Savage Jr. Black Jersey , the motivation behind the use of the signed rank test is that it provides a more precise analysis in situations where the distribution function can reasonably be taken to be symmetric.

The assumption of symmetry may be justified by an experimenter through observation or through an understanding of the nature of the data. For example, a simple histogram of the data may be sufficient to indicate that the assumption of symmetry is not inappropriate. In other cases, the experimenter may expect that the randomness in the data is composed primarily of a measurement error that can reasonably be expected to be distributed symmetrically. The additional precision of the signed rank test over the sign test is achieved through the consideration of the magnitudes of the deviations of the observed data values xi from the hypothesized median valueμ0. The sign test is performed by looking at whether an observation xi is less than μ0 or is greater than μ0. The signed rank test not only takes into account this information but also considers