Immunoassay based bioanalytical measurements are widely used in a variety of biomedical researchand clinical settings. In these settings they are assumed to faithfully represent the experimentalconditions being tested and the sample groups being compared. Although significant technicaladvances have been made in improving sensitivity and quality of the measurements, currently nometrics exist that objectively quantify the fidelity of the measured analytes with respect to noiseassociated with the specific assay. Here we introduce ratio of cross-coefficient-of-variation (rxCOV), afidelity metric for objectively assessing immunoassay analyte measurement quality when comparingits differential expression between different sample groups or experimental conditions. We derivethe metric from first principles and establish its feasibility and applicability using simulated andexperimental data. We show that rxCOV assesses fidelity independent of statistical significance,and importantly, identifies when latter is meaningful. We also discuss its importance in the contextof averaging experimental replicates for increasing signal to noise ratio. Finally, we demonstrate itsapplication in a Lynch Syndrome case study. We conclude by discussing its applicability to multiplexedimmunoassays, other biosensing assays, and to paired and unpaired data. We anticipate rxCOV to beadopted as a simple and easy-to-use fidelity metric for performing robust and reproducible biomedical research

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