Cited by 291Climate Change Communication and PerceptionRisk Perception and ManagementAcademic Publishing and Open Access

The DOI matches its Crossref record; 120 of the 120 references it cites were found in a registry or an index; 10 of the 75 sources its claims cite were checked for support.

A review of the effects of uncertainty in public science communication

Open access
Gustafson, Abel, Rice, Ronald E.·2020·Public Understanding of Science

Abstract

Uncertainty is inherent to science and science communication. However, the evidence appears mixed regarding whether portraying uncertainty in science communication has positive or negative effects. We review a diverse range of experimental literature ( k = 48; from 40 searches and 8000 retrievals), summarize the extant findings, and observe how the effects vary across four different types of communicated uncertainty ( deficient, technical, scientific, and consensus uncertainty). The results indicate that most findings of negative effects (such as reduced credibility and beliefs) are from experiments that operationalized uncertainty as disagreement or conflict in science ( consensus uncertainty). In this review, consensus uncertainty was never found to have positive effects. In contrast, uncertainty in the form of quantified error ranges and probabilities ( technical uncertainty) in these studies has had only positive or null effects, not negative effects. We also highlight frequent moderators of the effects of uncertainty, such as prior beliefs and worldviews.

Overview

AI snapshot

Written from the paper’s full text.

TL;DR

This systematic review synthesizes 48 papers containing 68 experiments and 38,947 participants on how portrayals of uncertainty in public science communication affect audience beliefs, attitudes, and behaviors. It finds that effects depend strongly on the type of uncertainty: consensus/conflict uncertainty is consistently negative, technical quantified uncertainty is positive or null, deficient uncertainty is mixed, and scientific uncertainty is rarely studied.

Key findings

  • Across 48 journal articles/book chapters and 68 experiments with 38,947 total participants, uncertainty portrayals showed negative, positive, and null effects depending on how uncertainty was operationalized.
  • Consensus uncertainty—expert disagreement, controversy, or conflicting evidence—was most clearly associated with negative main effects; the review found no instance of consensus uncertainty producing positive main effects.
  • Technical uncertainty—quantified error ranges, probabilities, or confidence intervals—showed no negative effects and instead had positive or null effects. Deficient uncertainty had mixed effects, while scientific uncertainty had mixed but tentative effects because it was studied infrequently.
  • Effects were often conditional on prior worldviews or topic opinions, trust in science/scientists, beliefs about uncertainty’s role in science, and topic. For example, expert consensus increased policy support only among people with high trust in scientists and decreased it among those with low trust; technical uncertainty lowered perceived risk relative to consensus uncertainty only among those high in deference to science.

Methods

  • Systematic review of experiments that manipulated uncertainty in a public-facing science message and compared outcomes with a no-uncertainty control, measuring beliefs, attitudes, or behaviors. Excluded non-experiments, private/interpersonal communication, comprehension outcomes, multi-message designs, and visual uncertainty depictions.
  • Searches used Google Scholar with 40 searches (eight uncertainty-related terms crossed with five science-communication variants), examining the first 200 results per search for 8,000 retrievals, plus reference and citation chasing. Final set: 48 journal articles/book chapters, 68 experiments, 38,947 participants.
  • Studies were coded by four uncertainty types (deficient, technical, consensus, scientific), sample type, topic, main effects, and conditional effects. The first author coded manifest and latent content, consulting the second author to resolve ambiguities.

Limitations

  • Stated: publication bias may affect the literature; many included studies used very small samples that may not replicate; inclusion criteria and search methods may have missed relevant experiments, including work in other fields or with different terminology; alternative uncertainty typologies or broader inclusion criteria could reveal different patterns; and heterogeneity across topics, stimuli, outcomes, samples, and years may confound conclusions.
  • Evident from the text: the review is a narrative synthesis rather than a quantitative meta-analysis, so effect sizes are not pooled; coding was primarily done by the first author with consultation rather than independent double-coding, and no formal inter-coder reliability statistic is reported.

Model-written summary. Check it against the paper before relying on it.

Integrity signals

Registry

Crossref · 10.1177/0963662520942122

Cites

120 of 120 references verified0 citation errors · 0 not checkable

Statistics

Nothing to recompute.

Verification

Claims the paper makes

32 No full text of the sources · 19 Own interpretation · 16 Own result · 13 No basis given · 12 Not checked yet · 12 Own method · 6 Partly supported · 5 Sources not found · 1 Supported · 1 Supported so far · 1 Not supported · accuracy not yet measured

Show all 57 claims

  • It may state two separate claims as one. 37
  • Probably not a substantial claim. 35
61 of 61
  • The paper relies on this without support.

    Even when reporting findings on which there is relative agreement, scientists often describe a degree of uncertainty surrounding a parameter.

    “Even when reporting findings on which there is relative agreement, scientists often describe a degree of uncertainty surrounding a parameter.”
  • The paper relies on this without support.

    Statistical science itself is an exercise in specifying uncertainty with precision by making estimations of variation and error.

    “Statistical science itself is an exercise in specifying uncertainty with precision by making estimations of variation and error.”
  • The paper relies on this without support.

    Scientists frequently present findings as probabilities or as estimates couched in error ranges.

    “scientists frequently present findings as probabilities or as estimates couched in error ranges.”
  • The paper relies on this without support.

    All science is subject to the inescapable possibility that in the future, our current best understandings may change in ways that are currently unknown and possibly unknowable.

    “All science is subject to the inescapable possibility that in the future, our current best understandings may change in ways that are currently unknown and possibly unknowable.”
  • The paper relies on this without support.

    Publication bias may affect the observations drawn from the current review.

    “One limitation to observations drawn from the current review is that of publication bias.”
  • The paper relies on this without support.

    Very small samples are fertile ground for generating effects that do not replicate.

    “which are fertile ground for generating effects that do not replicate”
  • The paper relies on this without support.

    The inclusion criteria or search methods may have excluded experiments that could help shine light on the question of the effects of uncertainty.

    “Another relevant limitation is that the inclusion criteria or search methods may have excluded experiments that could help shine light on the question of the effects of uncertainty.”
  • The paper relies on this without support.

    Different methods may have resulted in a slightly different set of studies.

    “Different methods may have resulted in a slightly different set of studies.”
  • The paper relies on this without support.

    Relevant research might be siloed in other fields using very different terminology, and might neither cite nor be cited by the studies included here.

    “It is also possible that relevant research might be siloed in other fields using very different terminology, and might neither cite nor be cited by the studies included here.”
9 of 9

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

Each contradiction between two passages listed here was confirmed by an independent check.

2 of 2

Citations

Cites

Research works printed but not linked: 6

  1. Carpenter RA, 1995, Environmental Professional, 17, 127
  2. Habicht FH, 1992, Guidance on Risk Characterization for Risk Managers and Risk Assessors
  3. Kuha M, 2009, Language & Ecology, 2, 1
  4. Parascandola M, 2000, Research Practitioner, 1, 1
  5. Stocking SH, 1999, Communicating Uncertainty: Media Coverage of New and Controversial Science, 23
  6. Winter S, 2015, Journal of Language and Social Psychology, 34, 251

Cited by

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Metadata

Authors
Gustafson, A., & Rice, R. E.
Affiliations
  • Yale University (US)
  • University of California, USA
Corresponding author
Gustafson, Abel
Venue
Public Understanding of Science
Published
2020-07-17
Volume
29
Issue
6
Pages
614-633
ISSN
  • 0963-6625 (ISSN-L)
  • 1361-6609
OpenAlex
W3042498646
Cited by
291
References
111
Citations by year
2026: 79 · 2025: 43 · 2024: 41 · 2023: 42 · 2022: 55 · 2021: 27 · 2020: 4
FWCI
42.77
Type
journal article
Language
en
Open access
Locations
  • Public Understanding of Science (publishedVersion)
  • PubMed (publishedVersion)
  • eScholarship (California Digital Library) (submittedVersion)
Funders
Arthur N. Rupe endowment
Topics
  • Social Sciences › Sociology and Political Science › Climate Change Communication and Perception
  • Social Sciences › Sociology and Political Science › Risk Perception and Management
  • Social Sciences › Library and Information Sciences › Academic Publishing and Open Access
Keywords
uncertainty communication, science communication, technological uncertainty, scientific uncertainty, credibility, public beliefs, prior beliefs, worldview, probability communication
MeSH terms
Communication, Dissent and Disputes, Uncertainty

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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

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