Anomaly detection often struggles to explain why something is considered an anomaly. This paper introduces a method called sequential explanation (SE) that helps analysts understand which features make a detected outlier point anomalous. The SE methods presented in the paper, outlier-based and sample-based, work alongside anomaly detectors to provide explanations. In experiments, both methods were found to effectively explain why outliers are anomalies and outperformed traditional feature explanations.