Modeling error in experimental assays using the bootstrap principle : 1 Understanding discrepancies between assays using di erent dispensing technologies

All experimental assaydata contains error, but themagnitude, type, andprimaryoriginof this error is o en not obvious. Here, we describe a simple set of assay modeling techniques based on the bootstrap principle that allow sources of error and bias to be simulated and propagated into assay results. We demonstrate how deceptively simple operations—such as the creation of a dilution series with a robotic liquid handler—can significantly amplify imprecision and even contribute substantially to bias. To illustrate these techniques, we review an example of how the choice of dispensing technology can impact assay measurements, and show how large contributions to discrepancies between assays can be easily understood and potentially corrected for. These simplemodeling techniques—illustratedwithanaccompanying IPythonnotebook—can allow modelers to understand the expected error and bias in experimental datasets, and even help experimentalists design assays to more e ectively reach accuracy and imprecision goals.