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Understanding data uncertainty.

Alisa Bokulich1, Wendy S Parker2

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Scientific data requires uncertainty estimates for completeness. This study proposes five philosophical theses on uncertainty estimation, emphasizing its adequacy-for-purpose for evaluating scientific data quality.

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Area of Science:

  • Philosophy of Science
  • Metrology
  • Data Science

Background:

  • Scientific data is often considered incomplete without uncertainty estimates.
  • The philosophy of data has largely overlooked the nature and importance of uncertainty estimation.
  • Existing practices in metrology offer a foundation for broader scientific data uncertainty estimation.

Purpose of the Study:

  • To adapt metrology's uncertainty estimation concepts for general scientific data.
  • To propose five philosophical theses regarding the nature and role of uncertainty estimates in data.
  • To introduce a novel 'adequacy-for-purpose' framework for evaluating uncertainty estimates.

Main Methods:

  • Conceptual analysis of uncertainty estimation in metrology.
  • Adaptation of metrological practices for broader scientific data.
  • Development and illustration of five philosophical theses on data uncertainty.
  • Case study using the GISTEMP global temperature dataset.

Main Results:

  • Uncertainty estimates are substantive, fallible, and iteratively improvable epistemic products.
  • The adequacy-for-purpose of uncertainty estimates is crucial for judging data adequacy.
  • The GISTEMP dataset serves as an example to illustrate the proposed theses.
  • A novel 'adequacy-for-purpose' view of uncertainty estimation is presented.

Conclusions:

  • Uncertainty estimation is essential for the completeness and evaluation of scientific data.
  • The proposed philosophical framework enhances understanding of data quality and reliability.
  • The study offers new perspectives on the safety versus precision debate in metrology.