Academic Journal
The distinction between systematic and random measurement errors depends on context and perspective.
| Τίτλος: | The distinction between systematic and random measurement errors depends on context and perspective. |
|---|---|
| Συγγραφείς: | Johansen JV; Radiometer Medical ApS, Copenhagen, Denmark. Electronic address: jesper.johansen@radiometer.dk., Theodorsson E; Department of Biomedical and Clinical Sciences, Linköping University, Linköping, Sweden. |
| Πηγή: | Clinica chimica acta; international journal of clinical chemistry [Clin Chim Acta] 2026 Sep 15; Vol. 591, pp. 121148. Date of Electronic Publication: 2026 Jun 05. |
| Τύπος έκδοσης: | Journal Article |
| Γλώσσα: | English |
| Στοιχεία περιοδικού: | Publisher: Elsevier Country of Publication: Netherlands NLM ID: 1302422 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1873-3492 (Electronic) Linking ISSN: 00098981 NLM ISO Abbreviation: Clin Chim Acta Subsets: MEDLINE |
| Imprint Name(s): | Original Publication: Amsterdam, Elsevier. |
| Ιατρικοί όροι (MeSH): | Uncertainty*, Bias |
| Περίληψη: | 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. (Copyright © 2026 Elsevier B.V. All rights reserved.) |
| Contributed Indexing: | Keywords: Bias; Imprecision; Random error; Systematic error; Total error; Uncertainty |
| Entry Date(s): | Date Created: 20260606 Date Completed: 20260811 Latest Revision: 20260812 |
| Update Code: | 20260813 |
| DOI: | 10.1016/j.cca.2026.121148 |
| PMID: | 42250674 |
| Βάση Δεδομένων: | MEDLINE |
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| Items | – Name: Title Label: Title Group: Ti Data: The distinction between systematic and random measurement errors depends on context and perspective. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AU" term="%22Johansen+JV%22">Johansen JV</searchLink>; Radiometer Medical ApS, Copenhagen, Denmark. Electronic address: jesper.johansen@radiometer.dk.<br /><searchLink fieldCode="AU" term="%22Theodorsson+E%22">Theodorsson E</searchLink>; Department of Biomedical and Clinical Sciences, Linköping University, Linköping, Sweden. – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%221302422%22">Clinica chimica acta; international journal of clinical chemistry</searchLink> [Clin Chim Acta] 2026 Sep 15; Vol. 591, pp. 121148. <i>Date of Electronic Publication: </i>2026 Jun 05. – Name: TypePub Label: Publication Type Group: TypPub Data: Journal Article – Name: Language Label: Language Group: Lang Data: English – Name: TitleSource Label: Journal Info Group: Src Data: <i>Publisher: </i><searchLink fieldCode="PB" term="%22Elsevier%22">Elsevier </searchLink><i>Country of Publication: </i>Netherlands <i>NLM ID: </i>1302422 <i>Publication Model: </i>Print-Electronic <i>Cited Medium: </i>Internet <i>ISSN: </i>1873-3492 (Electronic) <i>Linking ISSN: </i><searchLink fieldCode="IS" term="%2200098981%22">00098981 </searchLink><i>NLM ISO Abbreviation: </i>Clin Chim Acta <i>Subsets: </i>MEDLINE – Name: PublisherInfo Label: Imprint Name(s) Group: PubInfo Data: <i>Original Publication</i>: Amsterdam, Elsevier. – Name: SubjectMESH Label: MeSH Terms Group: Su Data: <searchLink fieldCode="MM" term="%22Uncertainty%22">Uncertainty*</searchLink><br /><searchLink fieldCode="MH" term="%22Bias%22">Bias</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: 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.<br /> (Copyright © 2026 Elsevier B.V. All rights reserved.) – Name: SubjectMinor Label: Contributed Indexing Group: Data: <i>Keywords: </i>Bias; Imprecision; Random error; Systematic error; Total error; Uncertainty – Name: DateEntry Label: Entry Date(s) Group: Date Data: <i>Date Created: </i>20260606 <i>Date Completed: </i>20260811 <i>Latest Revision: </i>20260812 – Name: DateUpdate Label: Update Code Group: Date Data: 20260813 – Name: DOI Label: DOI Group: ID Data: 10.1016/j.cca.2026.121148 – Name: AN Label: PMID Group: ID Data: 42250674 |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1016/j.cca.2026.121148 Languages: – Code: eng Text: English PhysicalDescription: Pagination: StartPage: 121148 Subjects: – SubjectFull: Bias Type: general – SubjectFull: Uncertainty Type: general Titles: – TitleFull: The distinction between systematic and random measurement errors depends on context and perspective. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Johansen JV – PersonEntity: Name: NameFull: Theodorsson E IsPartOfRelationships: – BibEntity: Dates: – D: 15 M: 09 Text: 2026 Sep 15 Type: published Y: 2026 Identifiers: – Type: issn-electronic Value: 1873-3492 Numbering: – Type: volume Value: 591 Titles: – TitleFull: Clinica chimica acta; international journal of clinical chemistry Type: main |
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