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    Minimal Detectable and Identifiable Biases for quality control

    265458.pdf (614.9Kb)
    Access Status
    Open access
    Authors
    Imparato, D.
    Teunissen, Peter
    Tiberius, C.
    Date
    2018
    Type
    Journal Article
    
    Metadata
    Show full item record
    Citation
    Imparato, D. and Teunissen, P. and Tiberius, C. 2018. Minimal Detectable and Identifiable Biases for quality control. Survey Review. 51 (367): pp. 289-299.
    Source Title
    Survey Review
    DOI
    10.1080/00396265.2018.1437947
    ISSN
    0039-6265
    School
    School of Earth and Planetary Sciences (EPS)
    Remarks

    This is an Author's Original Manuscript of an article published by Taylor & Francis in Survey Review on 1/3/2018 available online at http://www.tandfonline.com/10.1080/00396265.2018.1437947

    URI
    http://hdl.handle.net/20.500.11937/68003
    Collection
    • Curtin Research Publications
    Abstract

    The Minimal Detectable Bias (MDB) is an important diagnostic tool in data quality control. The MDB is traditionally computed for the case of testing the null hypothesis against a single alternative hypothesis. In the actual practice of statistical testing and data quality control, however, multiple alternative hypotheses are considered. We show that this has two important consequences for one's interpretation and use of the popular MDB. First, we demonstrate that care should be exercised in using the single-hypothesis-based MDB for the multiple hypotheses case. Second, we show that for identification purposes, not the MDB, but the Minimal Identifiable Bias (MIB) should be used as the proper diagnostic tool. We analyse the circumstances that drive the differences between the MDBs and MIBs, show how they can be computed using Monte Carlo simulation and illustrate by means of examples the significant differences that one can experience between detectability and identifiability.

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