All experimental assay data contains error arising from uncertainties in initial compositions, dispensed masses or volumes, measurement noise, model fitting error, and intrinsic biological variability. Accounting for this error to produce a reliable estimate of the uncertainty of experimentally-derived quantities is critical, as this is the basis for testing hypotheses or building predictive models, but it is often difficult to even identify the dominant sources of assay error, let alone propagate them. Our lab uses two primary tools to both build predictive models of assay error and incorporate all sources of error and uncertainty in data analysis: the bootstrap principle and Bayesian inference.