 The second type of normative standard assesses the accuracy of a judgment or decision making process, where the focus is getting the correct answer. Consider the accuracy of a categorical judgment, such as predicting whether a credit-card transaction is fraudulent (\(Y = 1\)) or legitimate (\(Y = 0\)). Classification accuracy is the number of correct predictions from all predictions made, which is often expressed as a ratio. But classification accuracy can yield a misleading assessment. For example, a method that always reported transactions as legitimate, \(Y = 0\), would in fact yield a very high accuracy score (&gt;97%) due to the very low rate (&lt;3%) of fraudulent credit card transactions. The problem here is that classification accuracy is a poor metric for problems that involve imbalanced classes with few positive instances (i.e., few cases where \(Y=1\)). More generally, a model with no predictive power can have high accuracy, and a model with comparatively lower accuracy can have greater predictive power. This observation is referred to as the accuracy paradox.