Test-based ruling, i.e. deciding whether an examinee whose test score is compared to some norm or threshold « passes » or « passes not », must cope with two uncertainties, two error sources: measurement error, associated with the test’s reliability index and corrupting somewhat the individual’s true score, and the sampling variability of the norm, a value generally based on a sample and slurred by its own error distribution. Following our study of the norm’s statistical properties and their control (Laurencelle, 2015), we now tackle measurement error and its interaction with the norm’s uncertainty, incorporating both in a mathematical system based generally on the normal distribution. The odds that an unqualified candidate be retained may be calculated, as may those of a qualified one be rejected. Finally, we propound the concept and methodology of the «safe norm » (Laurencelle, 2002), a device that makes possible to statistically control the risk of a decision error.