Up to this point we have discussed why QC is important, what materials are available, and how to incorporate them into experiments. Foremost, QC attempts to quantify instrument and experimental performance to give confidence to the validity, uncertainty, and accuracy of the resulting data. Secondary to this, QC metrics can be used to troubleshoot issues in sample preparation, data acquisition, and data analysis. We will not go into specifics on what deviation of QC metrics can indicate, but bracketing experimental and analytical steps with different QCs will aid in problem isolation. Also collecting longitudinal QC data is essential at defining acceptable norms for technical variation (i.e., statistical process control).