**5.4 Empirical quantification of false determination rates**

All of these tests are posed as null hypothesis tests. As such they only reject the null hypothesis at a particular level once sufficient evidence is found against it, and when the data size is limited, the power (the probability of correctly rejecting the null hypothesis) is similarly reduced. Therefore, in R2019 (page 100) the four tests were each tested separately for their false positive and false negative rates using a Monte Carlo method.

This aids interpretation since data segments vary in length. Before proceeding further, one may ask how meaningful the nominal *p-*values are, or as in R2019, one can determine the minimum data length required to allow acceptance of a finding of both UR and non-UR separately, for each test.
