**Generalisable findings** The notion of generalisability is used in a range of ways. At the broadest level, to generalise is to “make a general or broad statement by inferring from specific cases” (Stevenson 2015). Within the context of scientific experiments, Goodman et al., (2016) states that "the concept of generalizability (also known as transportability)... refers to the persistence of an effect in settings different from and outside of an experimental framework." It is this persistence of a finding outside the original experimental context that increases confidence that the effect is stable despite variations in experimental procedures and theoretical assumptions. For example, Schmidt (2009, 97) notes that when several studies change different variables that all corroborate the same finding as the initial study, then it supports generalising across these aspects. At a narrow level, assessments of the generalisability of a finding are often defined in terms extensions from a sample to a population. For example, within the social sciences: “The extent to which the findings of a statistical or other analysis of some sample can be reliably extrapolated from the data analysed to the population from which the sample was drawn. Two factors affect generalizability: the size of the sample and the representativeness of the sample. Generalizability is also a consideration even if one’s data are for a whole population—one might be interested in whether the result was generalizable to all possible populations (superpopulation generalizability) or perhaps simply to the same population at a different time (historical generalizability).” (Elliot et al. 2016a). While building on a similar focus on populations in the medical sciences, it is also recognised that the practice of generalising involves extending beyond statistical analyses to make inferences that may partly be subjective but not arbitrary - based on theory, judgment, and evidence external to the study (M. P. Porta 2016). The generalisability of a finding typically contributes to a sense of confidence that the finding is **[robust](https://osf.io/ytzdv/)**. For example, the definition in Frank Haroll’s (2019) [glossary of statistical terms](http://hbiostat.org/doc/glossary.pdf) states that: “Generalization means to operationalize the experiment and analysis differently, use new data, and get largely the same result...” *References listed* * Goodman, Steven N., Daniele Fanelli, and John P. A. Ioannidis. 2016. ‘What Does Research Reproducibility Mean?’ Science Translational Medicine 8 (341): 341ps12-341ps12. https://doi.org/10.1126/scitranslmed.aaf5027. * Porta, Miquel PortaMiquel. 2016. ‘Generalizability’. In A Dictionary of Epidemiology, edited by Miquel Porta. Oxford University Press. http://www.oxfordreference.com/view/10.1093/acref/9780199976720.001.0001/acref-9780199976720-e-830. * Schmidt, Stefan. 2009. ‘Shall We Really Do It Again? The Powerful Concept of Replication Is Neglected in the Social Sciences’. Review of General Psychology 13 (2): 90–100. https://doi.org/10.1037/a0015108. * Soler, Léna. 2012. ‘Robustness of Results and Robustness of Derivations: The Internal Architecture of a Solid Experimental Proof’. In Characterizing the Robustness of Science: After the Practice Turn in Philosophy of Science, edited by Léna Soler, Emiliano Trizio, Thomas Nickles, and William Wimsatt, 227–66. Boston Studies in the Philosophy of Science. Dordrecht: Springer Netherlands. https://doi.org/10.1007/978-94-007-2759-5_10. * Stevenson, Angus. 2015. ‘Generalize - Oxford Reference’. Oxford Dictionary of English (3 Ed.). 2015. http://www.oxfordreference.com/view/10.1093/acref/9780199571123.001.0001/m_en_gb0331070.