The basic principles on receiver-operating characteristic (ROC) analysis were discussed. ROC curves showed the discriminative ability of a test by the position of the full curve in a graph plotting the relation between the true positive rate (TPR) and the false positive rate (FPR) over a wide range of cut-off points of a test. The increase in the area under the ROC curve, or the shift of the curve upward and to the left in the diagram means that the test has better discriminative ability. A manual was given to conduct the ROC analysis with special reference to calculation of TPR, FPR, and the area under the ROC curve. Also discussed was the method to decide the best or optimal cut-off point as the positivity criterion of a test, based on the ROC analysis. Attention was paid to balance the risk of false negatives and false positives. We made an equation to decide the best cut-off point, which showed us the variables to be considered in the analysis of cut-off problems: the prevalence of disease and the outcome associated with each state classified by the test, i.e., true positive, false positive, true negative, and false negative.