Ethical Boundaries in Data Cleaning: Issues, Methods, and the Threat of Data Manipulation

Data integrity is essential to credible scientific research, yet practices such as data cleaning can lead to ethical concerns when conducted without clear methodological justification. This paper examines the ethical limits of data cleaning by reviewing common data issues, valid cleaning techniques, and the risks of manipulation that may compromise research validity. Using a qualitative literature review, the study finds that data cleaning is ethically acceptable only when supported by statistical reasoning, transparent documentation, and reproducible procedures. In contrast, removing or altering data to reinforce hypotheses or improve results constitutes manipulation and violates research ethics. The failure to distinguish these practices risks misinformation, weakened knowledge development, and diminished academic credibility. The findings underscore the need for strict data integrity practices across all research environments.

data cleaning, research ethics, data manipulation, data integrity, outliers

1. Abiteboul, S.; Clue, S.; Milo, T.; Mogilevsky, P.; Simeon, J.: Tools for Data Translation and Integration. In [26]:3-8, 1999. [Google Scholar] [Crossref]

2. Batini, C.; Lenzerini, M.; Navathe, S.B.: A Comparative Analysis of Methodologies for Database Schema Integration. In Computing Surveys 18(4):323-364, 1986. [Google Scholar] [Crossref]

3. Bernstein, P.A.; Bergstraesser, T.: Metadata Support for Data Transformation Using Microsoft Repository. In [26]:9-14, 1999 [Google Scholar] [Crossref]

4. Bernstein, P.A.; Dayal, U.: An Overview of Repository Technology. Proc. 20th VLDB, 1994. [Google Scholar] [Crossref]

5. Bouzeghoub, M.; Fabret, F.; Galhardas, H.; Pereira, J; Simon, E.; Matulovic, M.: Data Warehouse Refreshment. In [16]:47-67. [Google Scholar] [Crossref]

6. Chaudhuri, S., Dayal, U.: An Overview of Data Warehousing and OLAP Technology. ACM SIGMOD Record 26(1), 1997. [Google Scholar] [Crossref]

7. Cohen, W.: Integration of Heterogeneous Databases without Common Domains Using Queries Based Textual Similarity. Proc. ACM SIGMOD Conf. on Data Management, 1998. [Google Scholar] [Crossref]

8. Do, H.H.; Rahm, E.: On Metadata Interoperability in Data Warehouses. Techn. Report, Dept. of Computer Science, Univ. of Leipzig. http://dol.uni-leipzig.de/pub/2000-13. [Google Scholar] [Crossref]

9. Doan, A.H.; Domingos, P.; Levy, A.Y.: Learning Source Description for Data Integration. Proc. 3rd Intl. Workshop The Web and Databases (WebDB), 2000. [Google Scholar] [Crossref]

10. Fayyad, U.: Mining Database: Towards Algorithms for Knowledge Discovery. IEEE Techn. Bulletin Data Engineering 21(1), 1998. [Google Scholar] [Crossref]

11. Galhardas, H.; Florescu, D.; Shasha, D.; Simon, E.: Declaratively cleaning your data using AJAX. In Journees Bases de Donnees, Oct. 2000. http://caravel.inria.fr/~galharda/BDA.ps. [Google Scholar] [Crossref]

12. Galhardas, H.; Florescu, D.; Shasha, D.; Simon, E.: AJAX: An Extensible Data Cleaning Tool. Proc. ACM SIGMOD Conf., p. 590, 2000. [Google Scholar] [Crossref]

• The Impact Of UI/UX Design on User Trust and Task Completion in Civic Tech Platforms
• Solar Cell Photovoltaic Model Shell Sp 75
• Development of an Intelligent Traffic Management System to Address Visibility Obstruction at Urban Intersections: A Case Study of Ibadan Metropolis
• Optimum Placement of Facts Devices on an Interconnected Power Systems Using Particle Swarm Optimisation Technique
• Assessing Construction Transformation and Implication on Future Production Flow System