The distinction between systematic and random measurement errors depends on context and perspective

Measurement errors may be constant, change in predictable, systematic ways, or vary randomly and unpredictably. When discussing measurement errors, the distinction between systematic and random errors is often poorly characterized. Both systematic and random errors can occur at any stage of the testing process. Errors that occur during the measurement phase can be formally evaluated and quantified, for example, using CLSI evaluation protocol guidelines. Here, we assert that the assignment of measurement error labels, random and systematic, depends on the perspective of the observer and the availability of information. Random errors are usually quantified as imprecision or measurement uncertainty (MU), whereas systematic errors that remain approximately constant over time are quantified as biases. As more information becomes available, errors that were previously perceived as random may become predictable and thus considered systematic. Manufacturers/developers, users, and regulators of measurement procedures (MP) can have very different perspectives, including different levels of available knowledge, and these differences can affect perceptions of which errors are predictable. Therefore, whenever a bias is reported or discussed, its scope must be clearly stated. Similarly, whenever an imprecision or an uncertainty is stated, the sources of variation included in the estimate must also be detailed. Determining whether a source of error is systematic or random in each scenario ensures it can be meaningfully quantified and expressed.