This talk presents a statistical perspective on reproducible quantitative mass spectrometry-based proteomics. Statistical components of reproducibility include experimental design, from both biological perspective (which proteins and samples, and how many, do we need to quantify?) and technological perspective (are the assays appropriate for the task? Do the experimental steps run properly?). Statistical components of reproducibility also include data processing (which features should we use to quantify a protein?) and downstream statistical analysis (how to detect changes in protein abundance? Are our conclusions consistent with prior results?). Answer these questions requires the availability of statistical methods, and but also of publicly available data that help understand the advantages and the limitations of the methods. This talk will highlight the contributions of our lab to these components of reproducible research.