6/

The #HiddenUncertainty exemplified in [2] may suggest:

- each step (including "hidden steps") of the research process should be seen as non-trivial [https://www.youtube.com/watch?v=Hr4K5WdV8tI&t=1325s ]

- epistemic humility: different researchers performing "identical tasks" may not converge

(besides, this implies that each substantial data/model preparation/manipulation/validation deserves #authorship, as these steps are critical for the research findings and #accountability is needed: see https://credit.niso.org/ )

5/

A presentation on [2] by Breznau is available at https://www.youtube.com/watch?v=Hr4K5WdV8tI (archived: https://web.archive.org/web/20240107183600/https://www.youtube.com/watch?v=Hr4K5WdV8tI )

where a clear #counterexample (a key finding which does not suffer from uncertainty) is also provided -
to remark how, although #HiddenUncertainty into non-trivial data and models is a frequent issue scientists need to be aware of, important cases exist where core findings are evident and unambiguous:
human-driven #ClimateChange is a key one

(screenshot: https://www.youtube.com/watch?v=Hr4K5WdV8tI&t=134s )

3/

A partial illustration on #HiddenUncertainty influencing attempts to compress #complexity into #OneDimensionalMetrics may be "a large-scale crowdsourced research effort involving 73 teams" which found "that analyzing the same hypothesis with the same data can lead to substantial differences in statistical estimates and substantive conclusions" [2]

"Instead of convergence, teams’ results varied greatly, ranging from large negative to large positive effects"

See Fig 1
https://www.pnas.org/doi/10.1073/pnas.2203150119#fig01