Related Experiment Video
Updated: Jun 16, 2026

Advancing Dyslexia Assessment in Children Through Computerized Testing
Published on: August 16, 2024
Evaluating diagnostic tests: The area under the ROC curve and the balance of errors
1Imperial College, London. d.j.hand@imperial.ac.uk
Abstract:
Because accurate diagnosis lies at the heart of medicine, it is important to be able to evaluate the effectiveness of diagnostic tests. A variety of accuracy measures are used. One particularly widely used measure is the AUC, the area under the receiver operating characteristic (ROC) curve. This measure has a well-understood weakness when comparing ROC curves which cross. However, it also has the more fundamental weakness of failing to balance different kinds of misdiagnoses effectively. This is not merely an aspect of the inevitable arbitrariness in choosing a performance measure, but is a core property of the way the AUC is defined. This property is explored, and an alternative, the H measure, is described.
Related Concept Videos
Receiver Operating Characteristic Plot
Sensitivity, Specificity, and Predicted Value
Sensitivity is the...
Accuracy and Errors in Hypothesis Testing
In hypothesis testing, the probability of making a Type I error, denoted as α, is commonly set at 0.05. This significance level indicates a 5% chance...
Errors In Hypothesis Tests
Detection of Gross Error: The Q Test
Critical Region, Critical Values and Significance Level
In hypothesis testing, a sample statistic is converted to a test statistic using z, t, or chi-square distribution. A critical region is an area under the curve in probability distributions demarcated by the critical value. When the test statistic falls in this region, it suggests that the null hypothesis must be rejected. As this region contains all those values of the test...
