The development of biomarkers into diagnostic and prognostic tests can be categorized into three broad phases: discovery, performance evaluation, and impact determination when added to existing clinical measures. This talk covers some key concepts including classification types and classification metrics in performance evaluation, from a statistician’s perspective. The limitations of existing classification metrics and the importance of using appropriate classification metrics are highlighted. Specifically, this talk presents some new efficient biomarker evaluation metrics to address the common pitfall caused by “naïve pooling” in biomarker evaluation and the inefficiency of existing metrics in multiple ordered classification. Related statistical inference methods are also presented. An ovarian cancer data set from PLCO cancer study is analyzed.