The primary role of the clinical laboratory is to report accurate results for diagnosis of disease and management of illnesses. This goal has, to a large extent been achieved for routine biochemical tests, but not for immunoassays which remained susceptible to interference from endogenous immunoglobulin antibodies, causing false, and clinically misleading results. Clinicians regard all abnormal results including false ones as "pathological" necessitating further investigations, or concluding iniquitous diagnosis. Even more seriously, "false-negative" results may wrongly exclude pathology, thus denying patients' necessary treatment. Analytical error rate in immunoassays is relatively high, ranging from 0.4% to 4.0%. Because analytical interference from endogenous antibodies is confined to individuals' sera, it can be inconspicuous, pernicious, sporadic, and insidious because it cannot be detected by internal or external quality assessment procedures. An approach based on Bayesian reasoning can enhance the robustness of clinical validation in highlighting potentially erroneous immunoassay results. When this rational clinical/statistical approach is followed by analytical affirmative follow-up tests, it can help identifying inaccurate and clinically misleading immunoassay data even when they appear plausible and "not-unreasonable." This chapter is largely based on peer reviewed articles associated with and related to this approach. The first section underlines (without mathematical equations) the dominance and misuse of conventional statistics and the underuse of Bayesian paradigm and shows that laboratorians are intuitively (albeit unwittingly) practicing Bayesians. Secondly, because interference from endogenous antibodies is method's dependent (with numerous formats and different reagents), it is almost impossible to accurately assess its incidence in all differently formulated immunoassays and for each analytes/biomarkers. However, reiterating the basic concepts underpinning interference from endogenous antibodies can highlight why interference will remain analytically pernicious, sporadic, and an inveterate problem. The following section discuses various stratagems to reduce this source of inaccuracy in current immunoassay results including the role of Bayesian reasoning. Finally, the role of three commonly used follow-up affirmative tests and their interpretation in confirming analytical interference is discussed.