Measurement results of biological samples are not perfect and vary because of numerous factors related to the biological samples themselves and the measurement procedures used to analyze them. The imprecision in patients’ laboratory data arising from the measurement procedure, known as analytical variation, depends on the conditions under which the data are collected. Additionally, the sample type and sampling time significantly affect patients’ laboratory results, particularly in serial measurements using samples collected at different time points. For accurate interpretation of patients’ laboratory data, imprecision—both its analytical and biological components—should be properly evaluated and incorporated into data management. With advancements in measurement technologies, analytical imprecision can be minimized to an insignificant level compared to biological imprecision, which is inherent to all biomolecules because of the dynamic nature of metabolism. This review addresses: (i) the theoretical background of variation, (ii) the statistical and metrological evaluation of measurement variation, (iii) the assessment of variation under different conditions in medical laboratories, (iv) the impact of measurement variation on clinical decisions, (v) the influence of biases on measurement variation, and (vi) the variability of analytes in human metabolism. Collectively, both analytical and biological imprecision are inseparable aspects of all measurements in biological samples, with biological imprecision serving as the foundation of personalized laboratory medicine.

Physicians increasingly use laboratory-produced information for disease diagnosis, patient monitoring, treatment planning, and evaluations of treatment effectiveness. Bias is the systematic deviation of laboratory test results from the actual value, which can cause misdiagnosis or misestimation of disease prognosis and increase healthcare costs. Properly estimating and treating bias can help to reduce laboratory errors, improve patient safety, and considerably reduce healthcare costs. A bias that is statistically and medically significant should be eliminated or corrected. In this review, the theoretical aspects of bias based on metrological, statistical, laboratory, and biological variation principles are discussed. These principles are then applied to laboratory and diagnostic medicine for practical use from clinical perspectives.