A Type II error occurs when a test or model fails to identify a positive instance, mistakenly classifying it as negative. In statistical terms, it is failing to reject a false null hypothesis​In classification, a Type II error is essentially a false negative prediction. For instance, a cancer detection model missing a malignant tumor (classifying it as benign) is committing a Type II error. The frequency of Type II errors is related to the model’s recall (TPR) – a model with low recall has many Type II errors. In critical applications (like diagnostics), one often prioritizes reducing Type II errors (catching all true positives) even if it means tolerating more Type I errors. Balancing Type I and Type II errors is a central theme in setting decision thresholds and evaluating model performance for a given context.