Sikorski Michał, Gebharter Alexander, Osimani Barbara
Center for Philosophy, Science, and Policy, Marche Polytechnic University, Ancona, Italy.
J Eval Clin Pract. 2025 Jun;31(4):e70137. doi: 10.1111/jep.70137.
Despite its crucial role, sample representativeness remains a controversial topic in the methodology of medical science. There is an ongoing debate not only about how best to define and ensure the representativeness of a sample (e.g., Rudolph et al. 2023; Porta 2016), but also about whether representativeness is worth pursuing at all (e.g., Rothman et al. 2013).
Our aim is to construct a formalised, precise, and practical conceptualisation of sample representativeness.
We employ the established framework of causal Bayesian networks to develop such a conceptualisation.
We propose a precise formal definition of sample representativeness that translates into clear and actionable methodological guidance. Additionally, we provide examples and a checklist to illustrate the application of the proposed conceptualisation.
We believe that the presented definition will facilitate further discussion of the issue of representativeness and prove useful to scientists in practice.
尽管样本代表性起着关键作用，但在医学科学方法论中，它仍是一个有争议的话题。目前不仅存在关于如何最好地定义和确保样本代表性的争论（例如，鲁道夫等人，2023年；波尔塔，2016年），还存在关于代表性是否值得追求的争论（例如，罗斯曼等人，2013年）。
我们的目标是构建一个形式化、精确且实用的样本代表性概念。
我们采用因果贝叶斯网络的既定框架来发展这样一个概念。
我们提出了样本代表性的精确形式定义，该定义转化为清晰且可操作的方法指导。此外，我们提供了示例和清单来说明所提出概念的应用。
我们相信所提出的定义将促进对代表性问题的进一步讨论，并在实践中对科学家有用。
