A Type I error is an error where a test or model incorrectly flags an instance as positive when it is actually negative – a “false alarm”. In statistical terms, it is the incorrect rejection of a true null hypothesis​.In the context of binary classification, a Type I error corresponds to a false positive prediction. For example, an email spam filter marking a legitimate email as spam is a Type I error. The rate at which these errors occur is controlled by the model’s precision or specificity; reducing Type I errors often means being more conservative in declaring positives (which might increase Type II errors). It’s important to manage Type I errors in applications where false positives carry a high cost (e.g., misdiagnosing a healthy patient as sick can lead to unnecessary treatment).