Why Authors Don't Visualize Uncertainty

Jessica Hullman

REVIEW 5 major objections 5 minor 55 references

Why Authors Don't Visualize Uncertainty

T0 review · 5 major / 5 minor · reviewed 2026-08-14 · deepseek-v4-flash

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Pith's one-line read Omitting uncertainty from a chart is not neutral: viewers judge charts against imagined models, so without uncertainty the model is almost unspecified and showing uncertainty necessarily narrows interpretations.

desk verdict Empirically valuable study of why authors omit uncertainty, but the formal 'necessarily reduces degrees of freedom' claim is oversold and should be reframed as a testable hypothesis. read the letter →

arxiv 1908.01697 v1 pith:HQ5YEXGN submitted 2019-08-05 cs.HC

Jessica Hullman This is my paper · ORCID

classification cs.HC

keywords uncertaintyvisualizationrhetoricgraphicalstatisticalinferenceposteriorpredictivemodelcheckcommunicativeauthorsdatajournalismomission

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper asks why visualization authors so often omit uncertainty from charts meant to inform the public, and it argues that the omission is not a neutral simplification. Drawing on a survey of 90 visualization authors and interviews with 13 working designers and journalists, it documents a contradiction: most authors say uncertainty matters and should appear more often, yet a large share omit it, citing fear of confusing viewers, lack of data or skill, and concern that uncertainty would make results seem questionable. The paper's central theoretical claim is that a viewer judges a chart by comparing it to an imagined reference distribution, so when uncertainty is absent that reference distribution is left almost entirely open; showing uncertainty constrains it and thereby reduces the degrees of freedom in viewers' inferences. The accompanying rhetorical model explains how the omission persists: authors treat the chart as a pure signal, trust their analytical process to validate that signal, and treat uncertainty as a seam or question that threatens the message.

What carries the argument

The central mechanism is the implicit posterior predictive model check, a formal analogy between visual inspection and statistical model checking. A viewer of a chart is modeled as constructing an imagined distribution of replicated data $T(y_{\mathrm{rep}})$ from a model that depends on the observed data, the viewer's priors, and the viewer's assumptions about variance and comparison cases, and then judging how discrepant the displayed data are from that distribution. The argument uses this mechanism to convert the rhetorical observation that authors treat visualization as "signal" into a precise statement: with no uncertainty shown, the viewer's model specification is underdetermined, so uncertainty representation narrows the space of models a viewer can plausibly entertain. Constructed examples in the paper—a two-period two-party attitude chart and a multi-country debt-to-GDP line chart—show how varying the assumed variance or the comparison data changes the conclusion a viewer would draw.

What would settle it

Take a single chart designed to support an inference and render two versions, one without uncertainty and one with explicit intervals or a distribution, then measure the spread of viewers' conclusions or perceived signal strength across a large sample. The paper's claim predicts that the no-uncertainty version will produce a wider spread of inferences; observing no reduction when uncertainty is added would falsify the central claim. A more targeted version would ask viewers to identify which reference distribution the chart came from and test whether uncertainty annotations improve agreement.

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Extended reading notes

Core claim

The paper's claim is that uncertainty representation necessarily reduces the degrees of freedom in a viewer's statistical inferences, stated formally as "uncertainty representation necessarily constrains the set of possible models." In the proposed account, looking at a chart is an implicit posterior predictive model check: a viewer mentally generates a reference distribution $T(y_{\mathrm{rep}})$ from a model that is fit to the observed data and their prior beliefs, then compares the chart to that distribution to decide whether the signal is real. Without uncertainty, the viewer must choose variance, the data used to fit the model, the comparison set, and the model structure on their own, so different viewers can reach different conclusions from the same chart. Visualized uncertainty supplies information about the reference distribution the author had in mind, moving some of that interpretive burden from the viewer to the author; the paper grants that uncertainty does not eliminate ambiguity, because viewers can still misinterpret intervals or infer a different model than intended.

Load-bearing premise

The whole argument rests on the assumption that viewers actually judge charts by doing something like an implicit model check—mentally comparing the chart to an imagined set of replications—and that without uncertainty the space of models they can imagine is wide enough that different viewers will genuinely diverge; if real viewers do not run such comparisons, or if their mental model space stays narrow even without uncertainty, the conclusion that uncertainty necessarily reduces degrees of freedom does not follow.

Editorial extensions

If this is right

In a chart that omits uncertainty, viewers must fill in the missing statistical assumptions themselves; different viewers may therefore walk away with different conclusions from the same graphic.
Showing uncertainty narrows the set of interpretations but does not make interpretation unique: intervals can be misread as confidence limits or as data ranges, so authors still need to explain the reference model behind the uncertainty.
The rhetorical model implies that better uncertainty-encoding techniques alone will not change practice; authors' beliefs about signal, process, and credibility also have to shift.
Because the formal argument is logical rather than ethical, it offers a way to argue for uncertainty communication that does not depend on claims about moral duty.
Tools that help authors calculate, explain, and visualize uncertainty, and that let authors express the reference distribution behind their own analysis, are a direct practical consequence.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

An extension the paper does not develop: if omission leaves the reference distribution underdetermined, then the persuasive effect of a chart may depend as much on the priors viewers bring as on the data shown; tests could compare conclusions across viewers with different domain priors on the same no-uncertainty chart.
Another testable extension is whether the degrees-of-freedom argument changes author behavior: researchers could show authors the spread of interpretations their own charts produce with and without uncertainty and measure whether they then choose to include uncertainty.
The paper gestures toward decision-theoretic reasoning in its discussion; a fuller formal treatment would model the worst-case decision a viewer could make under omission versus inclusion, which would make the cost of omission concrete in specific policy or consumer settings.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

Two things you should know. This paper provides the first direct survey and interview evidence I know of on why visualization authors omit uncertainty, and that part is a real contribution. But the formal claim in the abstract—that uncertainty communication 'necessarily reduces degrees of freedom' in viewers' statistical inferences—is oversold; Section 6 demonstrates less than it asserts, and the body's own qualifiers contradict the abstract.

The empirical core is solid. A convenience sample of 90 authors plus 13 influential designers is not representative, and the author acknowledges the likely overrepresentation of uncertainty-sympathetic respondents. Even so, the results capture a clear contradiction: most authors say uncertainty should be shown more, yet nearly half admit to having left it out in the last year, mostly out of fear of confusing viewers, lack of access to uncertainty information, or not knowing how to compute it. The rhetorical model—visualization as signal, process as validator, uncertainty as obfuscating seam—is a useful synthesis that explains how the norm persists despite good intentions. The paper is honest about its limitations, and the qualitative analysis is carefully done.

The soft spot is the formal argument in Section 6. The adaptation of Gelman's graphical inference framework is suggestive, but 'necessarily reduces degrees of freedom' is under-derived. There is no operational definition of degrees of freedom or of the viewer's model space, and the argument rests on illustrative examples plus a hidden monotonicity assumption: that adding an uncertainty encoding always removes more inferential ambiguity than it introduces. That is doubtful, and the paper itself cites evidence that viewers systematically misinterpret confidence intervals and error bars. An interval can be read as a hard bound, a prediction range, a confidence interval, or deliberate imprecision; the mapping from display to reference distribution is not obviously less variable than from a bare point. The conclusion eventually hedges to 'reduces (though does not necessarily eliminate),' but the abstract and Section 6 opening still assert necessity. The claim should be reframed as a testable hypothesis.

Who should read this? People working on uncertainty visualization, visualization rhetoric, or HCI studies of author practice. The empirical findings and the rhetorical model deserve engagement; the formal part is a useful starting point once cut down to size. The paper deserves a serious referee, not a desk reject—with a request to tighten the formal claims, either by proving the monotonicity assumption or by stating the weaker variance-reduction hypothesis, and to share the survey instrument and anonymized data if possible.

Referee Report

5 major / 5 minor

Summary. This paper investigates why visualization authors omit uncertainty from their graphics. The author reports a survey of 90 visualization authors recruited via Twitter and interviews with 13 influential visualization practitioners, documenting a tension between authors' professed belief that uncertainty matters and their frequent omission of it. The paper contributes a three-tenet rhetorical model of uncertainty omission (visualization as signal; process validates signal; uncertainty obfuscates signal), and then adapts Gelman's graphical-inference framework to argue that uncertainty communication 'necessarily reduces degrees of freedom in viewers' statistical inferences.' It closes with recommendations for future tools and evaluations.

Significance. The empirical contribution is valuable: it is one of the few direct, systematic elicitations of practitioner rationales for uncertainty omission, and the paper is transparent about its recruitment, coding process, and sample limitations. The rhetorical model is a plausible and useful synthesis that can orient future HCI and visualization research. The formal model is a thought-provoking adaptation of existing statistical-inference ideas rather than a new derivation, and it is presented through illustrative examples. The main weakness is that the central formal claim is stated more strongly than the supporting argument and the manuscript's own caveats allow. Rephrasing the claim as 'can reduce degrees of freedom under a specified interpretation model' would make the paper defensible; the qualitative and rhetorical contributions do not need to change.

major comments (5)

[Section 6.1, Figure 4] The claim that uncertainty visualization 'necessarily reduces degrees of freedom' in viewers' inferences is not established by the formalism presented. No operational definition is given for 'degrees of freedom' or for the viewer's model space, and the argument is carried by constructed examples rather than a derivation. More importantly, the necessity claim relies on a hidden monotonicity assumption: that adding an uncertainty encoding always removes more interpretive ambiguity than it introduces. This assumption is doubtful given the paper's own citations [3,28], which show that viewers systematically misinterpret confidence intervals and error bars. Section 6.1.2 concedes that an uncertainty visualization does not ensure that viewers infer the author's intended reference distribution and that some audiences cannot reliably infer modeling assumptions from standard uncertainty displays. The logically supported conclusion is thus that uncertainty can reduce degrees of freedom under a specified interpretation model, not that it necessarily does. Because Section 6 presents this formalism as providing 'logical ground' against omission, this is a load-bearing issue.
[Abstract vs. Section 8] The abstract states that uncertainty communication 'necessarily reduces degrees of freedom in viewers' statistical inferences,' but the Conclusion says that uncertainty 'reduces (though does not necessarily eliminate) degrees of freedom in viewers' inferences.' These are different claims, and the manuscript should state one consistent claim. The strong 'necessarily' version is what drives the argument against omission, while the weaker version is what the analysis in Section 6.1.2 actually supports. The discrepancy should be resolved by tempering the abstract and Section 6's stronger formulations.
[Sections 3.1.2 and 7.0.1] The paper acknowledges in Section 3.1.2 and Section 7.0.1 that the survey and interview samples are convenience samples that likely overrepresent authors sympathetic to uncertainty visualization. This acknowledged limitation should be more carefully reflected in the scope of the rhetorical model. The three tenets in Section 5 are inferred from this sample, and the premise in Section 5.1 that uncertainty omission is a norm is supported partly by self-reported estimates from the same respondents. The empirical characterization is still a useful contribution, but the paper should frame the rhetorical model as a hypothesis about a specific population of social-media-recruited practitioners rather than as a general account of 'authors.'
[Section 6.1] The formal argument assumes that viewers judge visualized signal strength through an implicit posterior predictive model check, with a reference distribution p(yrep|y) drawn from a 'seemingly infinite' space in the absence of uncertainty. This assumption is asserted and illustrated, not tested empirically or derived from first principles. If viewers do not perform such model comparisons, or if their implicit model space is narrow even without uncertainty, the formal conclusion does not follow. The paper could strengthen the argument by providing at least a minimal formal characterization of the relevant model space and the interpretation mapping from an uncertainty display to p(yrep|y).
[Section 6.1.2] The sentence 'This reduces the amount of information that a viewer must mentally fill in, which would seem to necessarily reduce variance in interpretations across viewers' is a hedged intuition, not a proof. The hedge 'would seem to' indicates that the author is aware that the step is not fully demonstrated. The paper should either provide a concrete argument for why adding uncertainty information cannot introduce additional interpretive variance, or explicitly scope the claim to situations where viewers share the intended interpretation of the uncertainty encoding.

minor comments (5)

[Section 3.2] 'In gain a deeper understanding' should be 'To gain a deeper understanding.'
[Section 6.1] In the phrase 'T (rrep ) is drawn from a null plot distribution,' the symbol 'rrep' appears to be a typo for 'yrep.'
[Section 6.1.2] 'in is unlikely that all audiences could reliably infer' should be 'it is unlikely that all audiences could reliably infer.'
[Section 8] 'a formal model of of graphical statistical inference' contains a duplicated 'of.'
[Section 6.1.2] 'permissable' should be 'permissible.'

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity; the formal claim is an analytic adaptation of external graphical-inference theory, and the empirical results are self-contained.

full rationale

The paper's empirical contributions, including the survey/interview characterization and the rhetorical model of uncertainty omission, are grounded in participant responses and open coding rather than in the paper's own conclusions. The formal argument in Section 6 is explicitly presented as an adaptation of Gelman's external graphical-inference theory [24,25] and Buja et al. [6,7]; no parameter is fitted to a subset of data and then renamed a prediction, and no 'uniqueness theorem' is imported from the authors' own prior work. The claim that uncertainty representation constrains viewers' model space follows from the model's definitional setup: a viewer compares the visualization to an implicit reference distribution p(yrep|y), and encoding a reference distribution narrows the space of possible implicit models. This is an analytic conditional of the proposed framework, not a circular derivation, and the paper itself repeatedly concedes the limits of the claim. Section 6.1.2 states that 'an uncertainty visualization does not necessarily ensure that all viewers formulate T(yrep), p(yrep|y), or even T(y) in the same way,' and Section 8 softens the conclusion to 'uncertainty reduces (though does not necessarily eliminate) degrees of freedom in viewers' inferences.' The self-citations that appear, such as [29] for visualization rhetoric, [30] for uncertainty visualization evaluation, and [31,34,37] for uncertainty display techniques, are background references or pointers to prior technique papers; they are not load-bearing justifications of the central formal or empirical claims. No pattern of self-definition, fitted-input-called-prediction, self-citation-load-bearing, uniqueness-imported-from-authors, ansatz-smuggled-via-citation, or renaming-known-result is present. The main weakness is the strength of the word 'necessarily' in the abstract, but that is a correctness or scope concern about the argument's assumptions, not circularity.

Assumptions & free parameters 0 free parameters · 4 assumptions · 0 invented entities

No numeric free parameters are fitted. The paper's claims rest on qualitative and interpretive assumptions: the norm premise, the graphical-inference analogy, the supposed size of the viewer's model space, and the representativeness of the sample. These are stated or acknowledged in the text.

assumptions (4)

domain assumption Uncertainty omission is a norm in communicative visualization (Section 5.1).
The paper treats this as the premise for the rhetorical model. It is inferred from low reported rates of uncertainty use and interviewee statements, but the sample is a convenience sample and may overrepresent authors sympathetic to uncertainty.
domain assumption Viewers judge visualizations by implicit comparison to a reference distribution, modeled as a posterior predictive check (Section 6.1).
This is borrowed from Gelman and Buja et al., but applying it to communicative viewers is an analogy that is asserted with examples rather than empirically validated.
domain assumption Without explicit uncertainty, the viewer's model space is 'seemingly infinite,' so uncertainty necessarily reduces degrees of freedom (Section 6.1.2).
The phrase 'would seem to necessarily reduce' marks this as an assumption built into the argument, not a demonstrated theorem. It is close to the conclusion, but the paper does not test whether viewers actually experience a narrower model space when uncertainty is shown.
domain assumption Survey and interview respondents are sufficiently representative that their stated rationales reveal field-wide beliefs (Sections 3.1.2 and 7.0.1).
The paper acknowledges selection bias and heterogeneity, yet the rhetorical model is meant to explain a general norm. This requires an assumption that the themes in the sample reflect broader practitioner beliefs.

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Cite this review

Pith. "Pith review of Why Authors Don't Visualize Uncertainty." pith.science (2026). https://pith.science/paper/HQ5YEXGN

@misc{pith2026190801697,
  author       = {Pith},
  title        = {Pith review of: Why Authors Don't Visualize Uncertainty},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/HQ5YEXGN}},
  note         = {Machine review of arXiv:1908.01697}
}

read the original abstract

Clear presentation of uncertainty is an exception rather than rule in media articles, data-driven reports, and consumer applications, despite proposed techniques for communicating sources of uncertainty in data. This work considers, Why do so many visualization authors choose not to visualize uncertainty? I contribute a detailed characterization of practices, associations, and attitudes related to uncertainty communication among visualization authors, derived from the results of surveying 90 authors who regularly create visualizations for others as part of their work, and interviewing thirteen influential visualization designers. My results highlight challenges that authors face and expose assumptions and inconsistencies in beliefs about the role of uncertainty in visualization. In particular, a clear contradiction arises between authors' acknowledgment of the value of depicting uncertainty and the norm of omitting direct depiction of uncertainty. To help explain this contradiction, I present a rhetorical model of uncertainty omission in visualization-based communication. I also adapt a formal statistical model of how viewers judge the strength of a signal in a visualization to visualization-based communication, to argue that uncertainty communication necessarily reduces degrees of freedom in viewers' statistical inferences. I conclude with recommendations for how visualization research on uncertainty communication could better serve practitioners' current needs and values while deepening understanding of assumptions that reinforce uncertainty omission.

Figures

Figures reproduced from arXiv: 1908.01697 by the authors.

**Figure 2.** The list of audiences that survey respondents described creating visualizations for. Respondents could choose multiple answers. All survey responses were recorded via Google forms. Eleven of the interviews were transcribed by a professional transcriptionist. Two of the interviews were not recorded. To identify values and rationales in the results of the above activities, I started with open coding to identify them… view at source ↗

**Figure 3.** Survey participants’ ratings of their agreement with statements on a 5pt scale (1=Strongly Disagree, 5=Strongly Agree). [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗

Figures from the paper (2 more)

**Figure 4.** Figure 4: An adaption of a visualization presented by the Pew Founda [PITH_FULL_IMAGE:figures/full_fig_p007_4.png]

**Figure 5.** Figure 5: A visualization comparing government debt across countries. [PITH_FULL_IMAGE:figures/full_fig_p008_5.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

55 extracted references · 55 canonical work pages

[1]

Bastin, P

L. Bastin, P. F. Fisher, and J. Wood. Visualizing uncertainty in multi- spectral remotely sensed imagery.Computers & Geosciences, 28(3):337– 350, 2002

work page 2002
[2]

Beecham, J

R. Beecham, J. Dykes, W. Meulemans, A. Slingsby, C. Turkay, and J. Wood. Map lineups: effects of spatial structure on graphical inference. IEEE transactions on visualization and computer graphics , 23(1):391– 400, 2016

work page 2016
[3]

Belia, F

S. Belia, F. Fidler, J. Williams, and G. Cumming. Researchers misunder- stand conﬁdence intervals and standard error bars. Psychological meth- ods, 10(4):389, 2005

work page 2005
[4]

Boukhelifa and D

N. Boukhelifa and D. J. Duke. Uncertainty visualization: why might it fail? In CHI’09 Extended Abstracts on Human Factors in Computing Systems, pages 4051–4056. ACM, 2009

work page 2009
[5]

Boukhelifa, M.-E

N. Boukhelifa, M.-E. Perrin, S. Huron, and J. Eagan. How data work- ers cope with uncertainty: A task characterisation study. In Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems , pages 3645–3656. ACM, 2017

work page 2017
[6]

A. Buja, D. Asimov, C. Hurley, and J. A. McDonald. Elements of a viewing pipeline for data analysis. Dynamic graphics for statistics, pages 277–308, 1988

work page 1988
[7]

A. Buja, D. Cook, H. Hofmann, M. Lawrence, E.-K. Lee, D. F. Swayne, and H. Wickham. Statistical inference for exploratory data analysis and model diagnostics. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences , 367(1906):4361– 4383, 2009

work page 1906
[8]

A. Buja, D. Cook, and D. Swayne. Inference for data visualization. In Joint Statistics Meetings, August, 1999

work page 1999

Show all 55 references

[9]

Correll and M

M. Correll and M. Gleicher. Error bars considered harmful: Exploring al- ternate encodings for mean and error.Visualization and Computer Graph- ics, IEEE Transactions on, 20(12):2142–2151, 2014

2014
[10]

Correll, D

M. Correll, D. Moritz, and J. Heer. Value-suppressing uncertainty palettes. In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems, page 642. ACM, 2018

2018
[11]

J. W. Creswell, W. E. Hanson, V . L. Clark Plano, and A. Morales. Qual- itative research designs: Selection and implementation. The counseling psychologist, 35(2):236–264, 2007

2007
[12]

T. J. Davis and C. P. Keller. Modelling and visualizing multiple spatial uncertainties. Computers & Geosciences, 23(4):397–408, 1997

1997
[13]

de Finetti

B. de Finetti. Probabilities of probabilities: A real problem or a misun- derstanding. New Developments in the Applications of Bayesian methods, pages 1–10, 1977

1977
[14]

C. R. Ehlschlaeger, A. M. Shortridge, and M. F. Goodchild. Visualiz- ing spatial data uncertainty using animation. Computers & Geosciences, 23(4):387–395, 1997

1997
[15]

Erev and B

I. Erev and B. L. Cohen. Verbal versus numerical probabilities: Efﬁ- ciency, biases, and the preference paradox. Organizational behavior and human decision processes, 45(1):1–18, 1990

1990
[16]

Fernandes, L

M. Fernandes, L. Walls, S. Munson, J. Hullman, and M. Kay. Uncertainty displays using quantile dotplots or cdfs improve transit decision-making. In Proceedings of the 2018 CHI Conference on Human Factors in Com- puting Systems, page 144. ACM, 2018

2018
[17]

Finger and A

R. Finger and A. M. Bisantz. Utilizing graphical formats to convey un- certainty in a decision-making task. Theoretical Issues in Ergonomics Science, 3(1):1–25, 2002

2002
[18]

Fischhoff

B. Fischhoff. Risk perception and communication unplugged: twenty years of process 1. Risk analysis, 15(2):137–145, 1995

1995
[19]

Fischhoff

B. Fischhoff. Communicating uncertainty fulﬁlling the duty to inform. Issues in Science and Technology, 28(4):63–70, 2012

2012
[20]

Fischhoff and A

B. Fischhoff and A. L. Davis. Communicating scientiﬁc uncertainty. Proceedings of the National Academy of Sciences , 111(Supplement 4):13664–13671, 2014

2014
[21]

Fischhoff and D

B. Fischhoff and D. MacGregor. Subjective conﬁdence in forecasts. Jour- nal of Forecasting, 1(2):155–172, 1982

1982
[22]

Fischhoff, P

B. Fischhoff, P. Slovic, and S. Lichtenstein. Knowing with certainty: The appropriateness of extreme conﬁdence. Journal of Experimental Psychol- ogy: Human perception and performance, 3(4):552, 1977

1977
[23]

Gabry, D

J. Gabry, D. Simpson, A. Vehtari, M. Betancourt, and A. Gelman. Visu- alization in bayesian workﬂow. Journal of the Royal Statistical Society: Series A (Statistics in Society), 182(2):389–402, 2019

2019
[24]

A. Gelman. A bayesian formulation of exploratory data analysis and goodness-of-ﬁt testing*. International Statistical Review, 71(2):369–382, 2003

2003
[25]

A. Gelman. Exploratory data analysis for complex models. Journal of Computational and Graphical Statistics, 13(4):755–779, 2004

2004
[26]

D. M. Green, J. A. Swets, et al. Signal detection theory and psy- chophysics, volume 1. Wiley New York, 1966

1966
[27]

Griethe and H

H. Griethe and H. Schumann. The visualization of uncertain data: Meth- ods and problems. In SimVis, pages 143–156, 2006

2006
[28]

Hoekstra, R

R. Hoekstra, R. D. Morey, J. N. Rouder, and E.-J. Wagenmakers. Robust misinterpretation of conﬁdence intervals. Psychonomic bulletin & review, 21(5):1157–1164, 2014

2014
[29]

Hullman and N

J. Hullman and N. Diakopoulos. Visualization rhetoric: Framing effects in narrative visualization. IEEE transactions on visualization and com- puter graphics, 17(12):2231–2240, 2011

2011
[30]

Hullman, X

J. Hullman, X. Qiao, M. Correll, A. Kale, and M. Kay. In pursuit of error: A survey of uncertainty visualization evaluation. IEEE transactions on visualization and computer graphics, 25(1):903–913, 2019

2019
[31]

Hullman, P

J. Hullman, P. Resnick, and E. Adar. Hypothetical outcome plots outper- form error bars and violin plots for inferences about reliability of variable ordering. PloS one, 10(11):e0142444, 2015

2015
[32]

C. R. Johnson and A. R. Sanderson. A next step: Visualizing errors and uncertainty. IEEE Computer Graphics and Applications , 23(5):6– 10, 2003

2003
[33]

A. Kale, M. Kay, and J. Hullman. Decision-making under uncertainty in research synthesis: Designing for the garden of forking paths. 2019

2019
[34]

A. Kale, F. Nguyen, M. Kay, and J. Hullman. Hypothetical outcome plots help untrained observers judge trends in ambiguous data. IEEE transactions on visualization and computer graphics, 2018

2018
[35]

M. Kay, T. Kola, J. R. Hullman, and S. A. Munson. When (ish) is my bus?: User-centered visualizations of uncertainty in everyday, mobile pre- dictive systems. In Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems, pages 5092–5103. ACM, 2016

2016
[36]

M. W. Khaw, Z. Li, and M. Woodford. Risk aversion as a perceptual bias. Technical report, National Bureau of Economic Research, 2017

2017
[37]

Y .-S. Kim, L. Walls, P. Krafft, and J. Hullman. A bayesian cognition approach to improve data visualization. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems. ACM, 2019

2019
[38]

Lichtenstein, B

S. Lichtenstein, B. Fischhoff, and L. D. Phillips. Calibration of proba- bilities: The state of the art to 1980. Technical report, DECISION RE- SEARCH EUGENE OR, 1981

1980
[39]

Lipschitz and O

R. Lipschitz and O. Strauss. Coping with uncertainty: a naturalistic decision-making process. Organizational Behavior and Human Decision Processes, 69(2):149–163, 1997

1997
[40]

A. M. MacEachren. Visualizing uncertain information. Cartographic Perspectives, (13):10–19, 1992

1992
[41]

Majumder, H

M. Majumder, H. Hofmann, and D. Cook. Validation of visual statistical inference, applied to linear models. Journal of the American Statistical Association, 108(503):942–956, 2013

2013
[42]

C. F. Manski. Communicating uncertainty in ofﬁcial economic statistics: an appraisal ﬁfty years after morgenstern. Journal of Economic Litera- ture, 53(3):631–53, 2015

2015
[43]

C. F. Manski. Communicating uncertainty in policy analysis. Proceed- ings of the National Academy of Sciences, page 201722389, 2018

2018
[44]

C. F. Manski. The lure of incredible certitude. Technical report, National Bureau of Economic Research, 2018

2018
[45]

General government debt (indicator), 2019

OECD. General government debt (indicator), 2019

2019
[46]

M. J. Olson and D. V . Budescu. Patterns of preference for numerical and verbal probabilities. Journal of Behavioral Decision Making, 10(2):117– 131, 1997

1997
[47]

A. T. Pang, C. M. Wittenbrink, and S. K. Lodha. Approaches to uncer- tainty visualization. The Visual Computer, 13(8):370–390, 1997

1997
[48]

Political polarization in the american public, 2014

Pew Foundation. Political polarization in the american public, 2014

2014
[49]

Potter, M

K. Potter, M. Kirby, D. Xiu, and C. R. Johnson. Interactive visualization of probability and cumulative density functions.International journal for uncertainty quantiﬁcation, 2(4), 2012

2012
[50]

C. D. Schunn and J. G. Trafton. The psychology of uncertainty in scien- tiﬁc data analysis. Handbook of the psychology of science, page 461e483, 2012

2012
[51]

Skeels, B

M. Skeels, B. Lee, G. Smith, and G. G. Robertson. Revealing uncer- tainty for information visualization. Information Visualization, 9(1):70– 81, 2010

2010
[52]

Thomson, E

J. Thomson, E. Hetzler, A. MacEachren, M. Gahegan, and M. Pavel. A typology for visualizing uncertainty. In Electronic Imaging 2005, pages 146–157. International Society for Optics and Photonics, 2005

2005
[53]

M. Webster. Communicating climate change uncertainty to policy- makers and the public. Climatic Change, 61(1):1–8, 2003

2003
[54]

Wickham, D

H. Wickham, D. Cook, H. Hofmann, and A. Buja. Graphical inference for infovis. IEEE Transactions on Visualization and Computer Graphics, 16(6):973–979, 2010

2010
[55]

C. M. Wittenbrink, A. T. Pang, and S. K. Lodha. Glyphs for visualiz- ing uncertainty in vector ﬁelds. IEEE transactions on Visualization and Computer Graphics, 2(3):266–279, 1996

1996

Pith tools

Reviewed August 14, 2026 · model on record in the stance chip above.

[1] [1]

Bastin, P

L. Bastin, P. F. Fisher, and J. Wood. Visualizing uncertainty in multi- spectral remotely sensed imagery.Computers & Geosciences, 28(3):337– 350, 2002

work page 2002

[2] [2]

Beecham, J

R. Beecham, J. Dykes, W. Meulemans, A. Slingsby, C. Turkay, and J. Wood. Map lineups: effects of spatial structure on graphical inference. IEEE transactions on visualization and computer graphics , 23(1):391– 400, 2016

work page 2016

[3] [3]

Belia, F

S. Belia, F. Fidler, J. Williams, and G. Cumming. Researchers misunder- stand conﬁdence intervals and standard error bars. Psychological meth- ods, 10(4):389, 2005

work page 2005

[4] [4]

Boukhelifa and D

N. Boukhelifa and D. J. Duke. Uncertainty visualization: why might it fail? In CHI’09 Extended Abstracts on Human Factors in Computing Systems, pages 4051–4056. ACM, 2009

work page 2009

[5] [5]

Boukhelifa, M.-E

N. Boukhelifa, M.-E. Perrin, S. Huron, and J. Eagan. How data work- ers cope with uncertainty: A task characterisation study. In Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems , pages 3645–3656. ACM, 2017

work page 2017

[6] [6]

A. Buja, D. Asimov, C. Hurley, and J. A. McDonald. Elements of a viewing pipeline for data analysis. Dynamic graphics for statistics, pages 277–308, 1988

work page 1988

[7] [7]

A. Buja, D. Cook, H. Hofmann, M. Lawrence, E.-K. Lee, D. F. Swayne, and H. Wickham. Statistical inference for exploratory data analysis and model diagnostics. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences , 367(1906):4361– 4383, 2009

work page 1906

[8] [8]

A. Buja, D. Cook, and D. Swayne. Inference for data visualization. In Joint Statistics Meetings, August, 1999

work page 1999