An illusion of predictability in scientific results: Even experts confuse inferential uncertainty and outcome variability.

experiments science communication statistics uncertainty visualization

Journal

Proceedings of the National Academy of Sciences of the United States of America
ISSN: 1091-6490
Titre abrégé: Proc Natl Acad Sci U S A
Pays: United States
ID NLM: 7505876

Informations de publication

Date de publication:
15 Aug 2023
Historique:
medline: 9 8 2023
pubmed: 9 8 2023
entrez: 9 8 2023
Statut: ppublish

Résumé

Traditionally, scientists have placed more emphasis on communicating inferential uncertainty (i.e., the precision of statistical estimates) compared to outcome variability (i.e., the predictability of individual outcomes). Here, we show that this can lead to sizable misperceptions about the implications of scientific results. Specifically, we present three preregistered, randomized experiments where participants saw the same scientific findings visualized as showing only inferential uncertainty, only outcome variability, or both and answered questions about the size and importance of findings they were shown. Our results, composed of responses from medical professionals, professional data scientists, and tenure-track faculty, show that the prevalent form of visualizing only inferential uncertainty can lead to significant overestimates of treatment effects, even among highly trained experts. In contrast, we find that depicting both inferential uncertainty and outcome variability leads to more accurate perceptions of results while appearing to leave other subjective impressions of the results unchanged, on average.

Identifiants

pubmed: 37556500
doi: 10.1073/pnas.2302491120
pmc: PMC10438372
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

e2302491120

Subventions

Organisme : National Science Foundation (NSF)
ID : DGE 2040434

Références

PLoS Biol. 2015 Apr 22;13(4):e1002128
pubmed: 25901488
IEEE Trans Vis Comput Graph. 2014 Dec;20(12):2142-51
pubmed: 26356928
Br J Anaesth. 2003 Apr;90(4):514-6
pubmed: 12644429
Br Med J (Clin Res Ed). 1986 Mar 15;292(6522):746-50
pubmed: 3082422
Psychol Methods. 2005 Dec;10(4):389-96
pubmed: 16392994
Nat Methods. 2019 Jul;16(7):565-566
pubmed: 31217592
PLoS One. 2015 Nov 16;10(11):e0142444
pubmed: 26571487
J Pers Soc Psychol. 2000 Apr;78(4):772-90
pubmed: 10794380
Cortex. 2013 Mar;49(3):609-10
pubmed: 23347556
Wellcome Open Res. 2019 Apr 1;4:63
pubmed: 31069261
Nature. 2021 Jul;595(7866):181-188
pubmed: 34194044
Psychon Bull Rev. 2014 Oct;21(5):1157-64
pubmed: 24420726
Nat Methods. 2013 Oct;10(10):921-2
pubmed: 24161969
Nat Methods. 2014 Feb;11(2):119-20
pubmed: 24645192
Am Psychol. 2005 Feb-Mar;60(2):170-80
pubmed: 15740449
Psychol Methods. 2008 Mar;13(1):19-30
pubmed: 18331151
Psychon Bull Rev. 2012 Aug;19(4):601-7
pubmed: 22648655
J Cell Biol. 2007 Apr 9;177(1):7-11
pubmed: 17420288
IEEE Trans Vis Comput Graph. 2021 Feb;27(2):272-282
pubmed: 33048681

Auteurs

Sam Zhang (S)

Department of Applied Mathematics, University of Colorado, Boulder, CO 80309.

Patrick R Heck (PR)

Office of Research, Consumer Financial Protection Bureau, Washington, DC 20552.

Michelle N Meyer (MN)

Department of Bioethics & Decision Sciences, Geisinger Health System, Danville, PA 17822.

Christopher F Chabris (CF)

Department of Bioethics & Decision Sciences, Geisinger Health System, Danville, PA 17822.

Daniel G Goldstein (DG)

Microsoft Research, New York, NY 10012.

Jake M Hofman (JM)

Microsoft Research, New York, NY 10012.

Classifications MeSH