Reading a paper this morning https://onlinelibrary.wiley.com/doi/10.1002/pds.4295 that follows Nosek & Errington (2017) https://elifesciences.org/articles/23383.pdf in muddling the distinction between #replicability as a broader scientific aim vs #reproducibility as captured in the narrow (purely 'computational') idea of #reproducibleresearch discussed e.g. in a #Guix context here https://guix.gnu.org/cookbook/en/html_node/Reproducible-Research.html.
This mistake is not uncommon, and leads (in this present paper, at least) to insufficient stress on computational reproducibility as a sine qua non of any higher-order quality attribute.
![A snippet from 搂1.2 of 'Reporting to Improve Reproducibility and Facilitate Validity Assessment for Healthcare Database Studies V1.0', a report from the ISPE鈥怚SPOR Special Task Force on Real World Evidence in Health Care Decision Making.
"Reproducibility is a characteristic of a study or a finding. A reproducible study is one for which independent investigators implementing the same methods in the same data are able to obtain the same results (direct replication [38]). In contrast, a reproducible finding is a higher order target than a reproducible study, which can be tested by conducting multiple studies that evaluate the same question and estimand (target of inference) but use different data and/or apply different methodology or operational decisions (conceptual replication [38])"
Citation [38] here is Nosek & Errington (2017) https://elifesciences.org/articles/23383.pdf.](https://media.scicomm.xyz/media_attachments/files/115/677/521/023/257/746/original/972153bc6c37f83e.png)