Measurements are subject to random and systematic errors, yet almost no study in software engineering makes significant efforts in reporting these errors. Whilst established statistical techniques are well suited for the analysis of random error, such techniques are not valid in the presence of systematic errors. We propose a departure from de- facto methods of reporting results of technical debt measurements for more rigorous techniques drawn from established methods in the physical sciences. This line of inquiry focuses on technical debt calculations; however it can be generalized to quantitative software engineering studies. We pose research questions and seek answers to the identification of systematic errors in metric-based tools, as well as the reporting of such errors when subjected to propagation. Exploratory investigations reveal that the techniques suggested allow for the comparison of uncertainties that come from differing sources. We suggest the study of error propagation of technical debt is a worthwhile subject for further research and techniques seeded from the physical sciences present viable options that can be used in software engineering reporting.