The risk of normative bias in reporting empirical research: Lessons learned from prenatal screening studies about the prominence of acknowledged limitations

Panagiota Nakou, Rebecca Bennett

Research output: Contribution to journalArticlepeer-review

Abstract

Empirical data can be an extremely powerful and influential tool in bioethical research. However, when researchers or policy makers look for answers to ethical questions by engaging with empirical research, there can be a tendency (conscious or unconscious) to shape, report and use empirical research in a way that confirms their own preferred ethical conclusions. This skewing effect of what we call ‘normative bias’, is often so subtle it falls short of clear misconduct and thus can be difficult to call out. However, we argue that this subtle influence of bias has the potential to significantly influence debate and policy around highly sensitive ethical issues and must be guarded against. In this paper we share the lessons we have learned through a journey of self-reflection around the effect that normative bias can have when reporting and referring to empirical data relating to ethical issues. We use a variety of papers from our area of the ethics of routine prenatal screening to illustrate these subtle but often powerfully distorting effects of bias. Our aim in doing so is not to criticise the work of others, as we recognise our own normative bias, but to improve awareness of this issue, remind the need for reflexivity to guard against our own biases and introduce a new criterion, the idea of a ‘limitation prominence assessment’ that can work as a practical way in evaluating the seriousness of the limitations of an empirical study and thus, the risks of the study being misread or misinterpreted through superficial reading.
Original languageEnglish
Pages (from-to)589–606
Number of pages16
JournalTheoretical Medicine and Bioethics
Volume44
Issue number6
Early online date6 Nov 2023
DOIs
Publication statusPublished - 1 Dec 2023

Keywords

  • Empirical Bioethics, policy, prenatal screening, bias, reflexivity, data.

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