Using Narrative to Explain Uncertainty in Climate Change

Using Narrative to Explain Uncertainty in Climate Change | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Using Narrative to Explain Uncertainty in Climate Change Grace Freeman, Alina Rousseau, Michelle Brunton, Luke Kramer, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7860451/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This study presents the findings on the use of narrative when communicating complex scientific uncertainties. A growing body of evidence in narrative cognition and communication has shown promise for the use of narrative in science communication. The project researches the use of narrative in communicating the scientific uncertainty of data projection tools built to support agricultural and conservation decision makers with decisions related to climate change. Prior research has shown uncertainty is difficult to communicate and raises ethical concerns since scientists do not want audiences to be either underconfident in projections with epistemic uncertainty (unknowns remain) or overconfident in projections with aleatory uncertainty (based on random factors). Participants completed an empirical survey reviewing four data projection tools. For each tool, participants were randomly assigned to read either a technical or narrative description of the tool. Results indicate that narrative descriptions increased understanding, though measurements for emotional response and behavior change were non-significant. Results additionally indicate that the use of narrative did not reduce participants’ confidence in projections of epistemic uncertainty. However, participants did show a statistically significant overconfidence in the models’ ability to predict completely random factors, indicating that caution should be applied and further research conducted on the use of narrative in communicating scientific uncertainty. Earth and environmental sciences/Environmental social sciences Biological sciences/Psychology Social science/Psychology communication climate description narrative uncertainty epistemic aleatory Figures Figure 1 Background Climate change and data scientists have a powerful need to identify and implement effective communication strategies for their technical work. This need is imperative as agricultural and conservation decision-makers need understandable evidence to inform their decisions. Previous research has indicated strong promise with the use of narrative communication to facilitate the understanding of complex science. This study furthers that research by exploring whether narrative communication is an effective tool for communicating uncertainty data, since decision-makers may hesitate to act if they misunderstand or lose confidence in projection models. The BARRACUDA Project (Biodiversity And RuRal Adaptation to Climate change Using Data Analysis) is an interdisciplinary, multi-state, and multi-university research project in Northern New England seeking to create high-quality data models and tools to support agriculture and conservation sectors regarding climate change decision-making (RII Track-2 FEC; NSF Award #2019470). Within Barracuda, Team CHASM (Communication Has A Special Meaning) is a mentored undergraduate and graduate research team at the University of Maine at Augusta (UMA) seeking to gain a better understanding of effective climate change communication strategies. This study uses data projection tools created within the Barracuda Project as examples of uncertain information and empirically tests participants’ understanding, confidence, emotional response, and self-reported behavior change according to the use of either narrative or technical descriptions. Literature Review Communication fuels scientific dissemination. Communication is a channel by which sentiment, knowledge, evidence, and value can be expressed, received, and reflected (Filiz 2020; Hansson et al. 2020). Information can be communicated in a variety of ways; however, for the purpose of this paper, narrative and technical communication styles are the focus. Technical communication has been found to limit effective translation to non-scientists (Bromme et al. 2018; Schwingel 2018). Bullock et al. (2019) illustrate how the use of jargon can hinder the processing of scientific information and amplify resistance to the message. Narrative science communication is associated with increased recall, ease of understanding and comprehension, and shorter reading time (Dahlstrom 2014). Narrative communication can also engage multiple cognitions by emotionally transporting audiences such that they become deeply immersed in the narrative (Appel et al. 2015). By making science culturally relevant and applicable through storytelling, narrative communication may bridge the gap between scientific data and common public understanding (Dahlstrom 2014; Downs 2014). Narrative science communication’s power lies in its ability to make scientific concepts more relatable and memorable. Narratives engage listeners by creating a story that attracts attention, making the information more captivating and clearer to understand (Dahlstrom 2014; Downs 2014). This type of communication is particularly successful in reaching audiences who typically consume scientific data via mass media, which favors storytelling formats (Dahlstrom 2014). Thus, communication through a narrative lens can help to translate the relevance of science to everyday people, making the information actionable for audiences (Downs 2014). Through making information accessible and actionable, narratives can lead to intentional behavioral changes in participants (Kim et al. 2012). However, to our knowledge there are limited examples of narratives used to communicate uncertainty-inherent projections related to climate change. Rydmark et al. (2020) examined how audiences struggle with the concept of scientific uncertainty, often being unsure if projections are somewhat variable or largely unknown given the disparity between how scientists and the general population tend to communicate levels of uncertainty. Decision-makers may overestimate uncertainty and postpone decisions (Horne, et al., 2021) or misdirect resources (Ward, et al. 2019) due to this miscommunication. Furthermore, van der Bles et al. (2019) argued that scientists must differentiate between epistemic uncertainty, where there may be an abundance of evidence even though unknowns remain (e.g., climate change is underway; predictions vary because no model can include all data), and aleatory uncertainty, based on too many unknown or unpredictable variables such that it is equivalent to random chance (e.g., the weather forecast twenty years from next Saturday). The application of narrative science communication in this context offers a potential resolution. Narrative science communication may help the general population more fully grasp the nuances of uncertainty to make decisions in their lives. Methodology The Institutional Review Board (IRB) at the University of Maine at Augusta (UMA) classified as exempted from further review (IR 30195, approved 10/3/23) pursuant to 45 CFR 46.104(d) (2). Participants were adult university students in Psychology and Communications courses, age 18 to 65 ( n = 81). Among the participants who completed the demographic portion of the survey ( n = 65), 69% were female; 84% were White or Caucasian; 6% were Native; 5% were Hispanic; 2% were Black; and 2% were multiracial. Informed consent was obtained from all participants, and survey measures were set to not permit participants under the age of 18 years who may be taking college-level courses to access the survey. The survey was developed and hosted within Qualtrics, a web-based survey, data collection, and analysis platform (Qualtrics, Provo, UT). Participants were provided images to represent four data projection tools developed by the greater Barracuda Project research team to support New England agricultural and conservation workers to make decisions with uncertain climate change data. For each resource image, participants were randomly assigned to view descriptions of the associated data projection tools that were written in either a narrative or technical format. Four data projection tools with accompanying descriptions were used in this study: Spotted-Wing Drosophila, Soybean Crop Model, Crop Switching, and Data Visualization (Barracuda 2023). The images provided to participants were from internal communications within the grant team used in the development and discussions around these four tools. As an example of the study components, Fig. 1 shows the image associated with the Soybean Crop Model data projection tool. Participants randomly assigned to the narrative read a comedic short story about a hapless farmer whose neighbor helps him understand why he would be better off growing soybeans as opposed to pineapples based on the current and projected growing season changes. The narrative begins with “My neighbor Jerry says I’m a fool for trying to grow pineapples in Northern New England, but I think he just doesn’t have the courage to go where no one has ever gone before.” The exasperated neighbor brings the hapless farmer the Soybean Crop Model and explains what data informs the model as well as how to understand the biomass projections. Participants randomly assigned to the technical description read the same information about the model inputs and outputs, though in a technical and data rich format. The technical descriptions are straightforward and instructive. The Soybean Crop Model description began, “The plot in panel A shows the number of days from planting it took the plot of soybeans to reach each of the five important development stages from emergence (sprouts emerging from the soil) to maturity (plants ready for harvest).” Predictive models have inherent uncertainty. The epistemic uncertainty stems from the inability of any model to encompass the infinite variables that can affect the target outcome; decisions must always be made as to which variables to include or exclude in the simplified models. The narrative and technical descriptions include information about the specific data included in the model, and describes the prediction outputs. For example, the Soybean Crop Model predicts crop yield per square meter under ideal climate conditions, and is informed by temperature, solar radiation, and soil moisture content data (but not, for example, soil quality, the use of fertilizers, pesticides, crop rotations, or dozens of other factors). The aleatory uncertainty stems from the many other unpredictable factors that can also affect crop outcomes, such as deer infestations, wildfires, or bad seed stock. These factors are essentially random events that cannot be predicted in a crop yield model. After reviewing the uncertainty information, all participants were given the same questions to measure their understanding, regardless of whether they received the information in the narrative or technical format. Participants were then asked additional questions designed to measure their emotional response/transportation (immersion into a narrative) and to evaluate their judgements of confidence in the uncertainty information presented. Each of these question sets were developed by the team according to each specific data projection tool. The questions were presented as true/false to check respondents' understanding of the material and Likert-scaled questions were used to assess the confidence level of respondents after they completed the readings. The emotional response/transportation questions were adapted from a short form transportation scale (Appel et al. 2015) which measures self-reported emotional, imaginative, and cognitive engagement with the descriptions. Analysis of the data collected from this survey was conducted with PSPP software (GNU Project 2007). The survey experienced significant attrition, such that later portions of the survey had lower completion rates. Therefore, the team combined all scenarios to create a more robust sample size when analyzing the difference in outcomes between narrative and technical reporting. The overall aim of this study was to discover if narrative descriptions were more effective than technical descriptions at communicating uncertain scientific data. It was hypothesized that participants receiving the narrative versus technical description would 1) better understand, 2) have a stronger emotional response, and 3) a stronger likelihood of belief or behavior change, without 4) undermining their confidence in the uncertain data. Results Regarding hypothesis 1 (that participants will better understand the uncertain scientific data when communicated in narrative versus technical format), the one-way ANOVA revealed a modest but significantly better understanding of the science when participants read a narrative reporting formats ( n = 153) versus technical reporting formats ( n = 150) (F(1, 301) = 6.22, p = .013*). Each data projection tool scenario had three questions that checked for understanding of the reported science, leading to a correct understanding score between 0 (no correct answers) and 3 (all correct answers). Participants who read the narrative scenarios scored more 2s and 3s, showing increased understanding of the information (Table 1). Table 1 . Number of correct answers for all narrative versus all technical description responses Number of correct answers Total 0 1 2 3 Narrative n 3 25 58 67 153 Row % 2.0% 16.3% 37.9% 43.8% 100.0% Column % 27.3% 41.0% 51.3% 56.8% 50.5% Total % 1.0% 8.3% 19.1% 22.1% 50.5% Technical n 8 36 55 51 150 Row % 5.3% 24.0% 36.7% 34.0% 100.0% Column % 72.7% 59.0% 48.7% 43.2% 49.5% Total % 2.6% 11.9% 18.2% 16.8% 49.5% Total n 11 61 113 118 303 Row % 3.6% 20.1% 37.3% 38.9% 100.0% Column % 100.0% 100.0% 100.0% 100.0% 100.0% Total % 3.6% 20.1% 37.3% 38.9% 100.0% Additionally, when participants were asked if the descriptions they read were easy or hard to understand, participants reported perceiving that the narrative explanations were easier to understand (Table 2). A Chi Square analysis revealed a significant difference in the perception of ease versus difficulty of the readings between participants who read narrative versus technical descriptions (χ² (df = 1, n = 258) = 9.84, p = .002**). Table 2. Participant perception of description difficulty Easy v. Hard Easy Hard Total Narrative n 97 30 127 Row % 76.4% 23.6% 100.0% Column % 56.1% 35.3% 49.2% Total % 37.6% 11.6% 49.2% Technical n 76 55 131 Row % 58.0% 42.0% 100.0% Column % 43.9% 64.7% 50.8% Total % 29.5% 21.3% 50.8% Total n 173 85 258 Row % 67.1% 32.9% 100.0% Column % 100.0% 100.0% 100.0% Total % 67.1% 32.9% 100.0% Hypothesis 2 (that participants would have a stronger emotional response to narrative versus technical writing) found no significant differences (F(1, 209) = 2.46, p = .118). These results indicate that participants who read the narrative descriptions were no more emotionally transported (i.e., invested and immersed) than those who read the technical descriptions. Hypothesis 3 (that participants would show a stronger likelihood of belief or behavior change) was measured by asking participants two yes or no questions: “Do you perceive this type of data projection tool would be useful to you or someone you know?” and “If you had access to this tool would you use it to inform your decision-making?” A Chi Square analysis on perceived usefulness of each respective data projection tool yielded significant results, χ² (df = 1, n = 277) = 15.06, p = .000** (Table 3), such that participants receiving the narrative descriptions found the tools to be more useful than did the participants reading the technical descriptions. Table 3. Perceived usefulness of the data uncertainty tools Useful Yes No Total Narrative n 108 36 144 Row % 75.0% 25.0% 100.0% Column % 60.7% 36.4% 52.0% Total % 39.0% 13.0% 52.0% Technical n 70 63 133 Row % 52.6% 47.4% 100.0% Column % 39.3% 63.6% 48.0% Total % 25.3% 22.7% 48.0% Total n 178 99 277 Row % 64.3% 35.7% 100.0% Column % 100.0% 100.0% 100.0% Total % 64.3% 35.7% 100.0% However, believing that the tools would be useful did not lead to participants reporting a likeliness to actually utilize the tools. A Chi Square analysis did not reveal evidence of perceived likelihood of behavior change to use the tools χ² (df = 1, n = 282) = 1.82, p = .178 (Table 4). Table 4. Perceived likelihood of behavior change to use the tools Behavior Change Yes No Total Narrative n 91 55 146 Row % 62.3% 37.6% 100.0% Column % 55.2% 47.0% 51.8% Total % 32.3% 19.5% 51.8% Technical n 74 62 136 Row % 54.4% 45.6% 100.0% Column % 44.8% 53.0% 48.2% Total % 26.2% 22.0% 48.2% Total n 165 117 282 Row % 58.5% 41.5% 100.0% Column % 100.0% 100.0% 100.0% Total % 58.5% 41.5% 100.0% Hypothesis 4 (that the use of narrative descriptions would not undermine participants’ confidence in uncertain data) was this study’s new application of the use of narrative in scientific communication. The survey included confidence questions for both epistemic and aleatory uncertainty related to each data projection tool. As an example, the check in confidence held for the Soybean Crop Model despite epistemic uncertainty asked, “How confident are you that the model can predict harvestable biomass of soybeans under ideal conditions?” We hypothesized that the narrative descriptions would not undermine participants’ confidence in the scientific data. Indeed, a Chi Square analysis of all combined scenarios revealed no evidence of a difference in confidence level when participants read the narrative versus technical descriptions, χ² (df = 4, n = 258) = 6.16, p = .187 (Table 5). Table 5 . Participant confidence in the tools when asked questions regarding epistemic uncertainty Very Confident Confident Neutral Unconfident Very Unconfident Total Narrative n 15 68 37 7 0 127 Row % 11.8% 53.5% 29.1% 5.5% .0% 100.0% Col. % 71.4% 47.9% 45.1% 58.3% .0% 49.2% Total % 5.8% 26.4% 14.3% 2.7% .0% 49.2% Technical n 6 74 45 5 1 131 Row % 4.6% 56.5% 34.4% 3.8% .8% 100.0% Col. % 28.6% 52.1% 54.9% 41.7% 100.0% 50.8% Total % 2.3% 28.7% 17.4% 1.9% .4% 50.8% Total n 21 142 82 12 1 258 Row % 8.1% 55.0% 31.8% 4.7% .4% 100.0% Col.% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% Total % 8.1% 55.0% 31.8% 4.7% .4% 100.0% While the data indicated that narrative descriptions of the science did not undermine the confidence of participants in uncertain data of an epistemic nature, the same was unfortunately true for uncertain data of an aleatory nature. For example, the check for confidence (or more specifically, overconfidence) in the Soybean Crop Model’s ability to predict unpredictable outcomes asked, “How confident are you that the model can predict harvestable biomass of soybeans under unforeseen circumstances like deer attacking the crop?” All of the models’ narrative and technical descriptions included the same explicit information about what data was used in the model to create the projections. In this case of the Soybean Crop Model, both descriptions stated that the model was based on temperature and solar radiation each day, as well as the soil moisture content. Deer infestations were therefore outside of the scope of the model. Participants who read the technical description were more likely to recognize that the models could not account for such random factors. Participants who read the narrative descriptions were less likely to recognize that random variables were beyond the scope of the science, χ² (df = 4, n = 231) = 10.70, p = .03* (See Table 6). This finding is of concern because it indicates that while audience confidence in uncertainty data may not be undermined by the use of narrative scientific description, the audience’s complex understanding of the scope and implications of the uncertainties may be adversely affected. Table 6 . Participant confidence in the tools when asked questions regarding aleatory uncertainty Very Confident Confident Neutral Unconfident Very Unconfident Total Narrative n 1 42 64 12 3 122 Row % .8% 34.4% 52.5% 9.8% 2.5% 100.0% Col. % 12.5% 54.5% 59.8% 37.5% 42.9% 52.8% Total % .4% 18.2% 27.7% 5.2% 1.3% 52.8% Technical n 7 35 43 20 4 109 Row % 6.4% 32.1% 39.4% 18.3% 3.7% 100.0% Col. % 87.5% 45.5% 40.2% 62.5% 57.1% 47.2% Total % 3.0% 15.2% 18.6% 8.7% 1.7% 47.2% Total n 8 77 107 32 7 231 Row % 3.5% 33.3% 46.3% 13.9% 3.0% 100.0% Col.% 100% 100.0% 100.0% 100.0% 100.0% 100.0% Total % 3.5% 33.3% 46.3% 13.9% 3.0% 100.0% Discussion The study results indicate that narrative descriptions of scientific uncertainty data can facilitate better comprehension of information, making them a valuable tool for instructional purposes. Likewise, findings indicate that participants perceive narratives as easier to understand as opposed to technical communication. The increased comprehension validates previous research (Dahlstrom 2014; Downs 2014) that narratives can be used to promote engagement with a wider audience, increasing the accessibility of science to the general population. The study found no significant difference in participants’ emotional transportation. That is to say, participants who read the narrative descriptions were no more and no less transported than those who read the technical descriptions. While narrative does have an established capacity for transporting audiences to the extent that they are wholly absorbed in the story (Appel et al. 2015), the narratives used in this study did not establish that effect among participants. Future research can explore the impact of narratives that resonate more and less with participants’ personal experiences, values, or concerns. Audiences who experience greater transportation may form emotional connections with the story presented, which may lead to increased behavior change or other communication outcomes in addition to increased understanding of the material. The study did not replicate the increases in behavior change associated with the use of narrative descriptions that has been found in the literature (Downs 2014; Kim et al. 2012). While participants who read the narrative descriptions found the data projection tools to be more useful than those who read the technical descriptions, they did not indicate that they would change their behavior to make use of the tool. The lack of reported behavior change may be due to the lack of transportation into the narrative, or it may be due to our participant pool being composed of college students that includes both individuals who are likely to use the data projection tool in their careers and those who are unlikely to have the need. Alternatively, the empirical survey format we used may have limited the way behavior change could be measured, and simply may not be able to capture the increase in behavior change found in the literature (Kim et al. 2012). Future research could particularly focus on populations who are more likely to have use for the tool in addition to exploring the use of more and less transporting narratives and adopting measures that more comprehensively capture behavior change. This study explored how the use of narrative in science communication might impact an audience’s understanding of and confidence in uncertain projections. Scientific uncertainty has been shown to be a specific area of struggle for audiences, with decision-makers over-emphasizing uncertain projections and subsequently delaying urgent action (Horne, et al. 2021) or misdirecting resource allocation (Ward, et al. 2019). Epistemic uncertainty in particular needs to be better understood so the public can have confidence in robust projections. This is because understanding epistemic uncertainty may help people see why a projection looks the way it does and how it can become more trustworthy over time. The fact that scientists must make informed decisions about what to include and exclude in a predictive model will necessarily leave unknowns in any model. However, each predictive model adds to a body of evidence that need not be discounted whole cloth due to ongoing epistemic uncertainty. The results indicate that audience confidence in epistemic uncertainty data was not impacted by the use of narrative descriptions. This is promising for advances in science communication. The results also included a concerning caveat. Participants who read narrative descriptions were less likely to recognize the limitations that the data projection models possess in predictions concerning random factors beyond the model scope. While there was little difference between participants reporting they were confident or very confident in the models’ ability to predict truly random factors (Narrative 35.2% versus Technical 38.5%), participants who read the technical descriptions were more likely to be unconfident and very unconfident (22.0%) than those who read narrative descriptions (12.3%). It is possible that this disparity resulted from narrative descriptions invoking a sense of fictionality in participants’ understanding, such that the possibilities and limitations of science were perceived to change. All narrative descriptions in this study were fictional but not fantastical, providing plausible interactions of real people with the uncertain projection tools. Nevertheless, the very use of narrative may lead audiences to suspend disbelief and entertain applications inconsistent with reality (e.g., a model predicting yield in ideal growing conditions is able to predict a deer infestation). As such, future research should explore whether suspension of disbelief can impact an audience’s cognitive adherence to scientific laws in the context of fictional or non-fictional scientific narratives. It is also possible that this disparity occurred because reading technical descriptions invoked a higher level of critical thinking, such that audiences were more likely to consider scientific implications of data when they were actively engaged with scientific language. Future research should explore whether different thought processes, including critical thinking, are engaged sufficiently with narrative science communication to sufficiently and ethically communicate uncertain information. Increased understanding of uncertainty data is crucial, but not if audiences lose the impulse to read critically and consider the realistic limits of the science. Conclusion This study sought to use narrative communication as a possible solution to the challenges faced by science communicators who disseminate uncertainty projection models. It was hypothesized that participants receiving the narrative versus technical description format would 1) better understand, 2) have a stronger emotional response, and 3) a stronger likelihood of belief or behavior change, without 4) undermining their confidence in the uncertain data. We found that narrative descriptions improved participants’ understanding of scientific information. In the same vein, participants also perceived narratives to be easier to understand. However, our data does not provide clear support that narrative communication can be successfully applied to the communication of uncertainty data. Participants’ confidence in uncertain data did not lessen with narrative descriptions, though participants did show an overconfidence in predictions beyond the scope of the models. Similarly, our data does not support the idea that participants will alter their behavior nor will they have a strong emotional response when presented with narratives about data projection tools. In light of these findings, using narrative communication styles should be approached with an awareness of its potential pitfalls in explaining uncertainty. However, the power of narrative science communication should not be discarded. Using caution, and with additional research as to its appropriate use with uncertain data, narrative science communication can serve as an instrument to foster scientific literacy in broader communities. Declarations Ethics approval The questionnaire and methodology for this study was approved by the Institutional Review Board committee of the University of Maine at Augusta (IR 30195) which recommend this proposal be exempted from further review pursuant to 45 CFR 46.104(d) (2) “[r]esearch only including interactions involving educational tests, survey procedures, interview procedures, or observation of public behavior”. Competing Interest The authors have no relevant financial or non-financial interests to disclose. Consent to participate Informed consent was obtained from all individual participants included in the study. Funding This work was supported by a National Science Foundation Grant (RII Track-2 FEC; NSF Award #2019470). Author Contribution All authors contributed to the study conception and design, including material preparation, data collection, and analysis. Stephanie Miller developed technical descriptions used in the survey. Luke Kramer, Michelle Brunton, Grace Freeman, Alina Rousseau, and Laura K. Corlew developed narrative descriptions used in the survey. Laura K. Corlew and Stephanie Miller provided academic support and professional advising at all stages. Laura K. Corlew, Grace Freeman, and Alina Rousseau wrote the main manuscript text. All authors commented on previous versions of the manuscript. All authors read and approved the final manuscript. Acknowledgement The members of the Barracuda research team developed the data projection models and associated images that were used in the survey: Brian McGill, PI, Nicholas Gotelli, co-PI, Matthew Dube, Co-PI, Timothy Waring, Co-PI, Meredith Niles, Co-PI, Laurent Hebert-Dufresne, SKP Data Availability [https://www.openicpsr.org/openicpsr/workspace?goToPath=/openicpsr/228762&goToLevel=project](https:/www.openicpsr.org/openicpsr/workspace?goToPath=/openicpsr/228762&goToLevel=project) References Appel, M., Gnambs, T., Green, M., & Richter, T. (2015). 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Mammal Review , 50 (2), 211–220. https://doi.org/10.1111/mam.12188 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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Augusta","correspondingAuthor":false,"prefix":"","firstName":"Alina","middleName":"","lastName":"Rousseau","suffix":""},{"id":544470542,"identity":"fd3af86b-ff9d-4776-9545-55f39b3841d6","order_by":2,"name":"Michelle Brunton","email":"","orcid":"","institution":"University of Maine at Augusta","correspondingAuthor":false,"prefix":"","firstName":"Michelle","middleName":"","lastName":"Brunton","suffix":""},{"id":544470543,"identity":"ae8d5f5c-b95b-4730-b5a1-8d7b6a867d8b","order_by":3,"name":"Luke Kramer","email":"","orcid":"","institution":"University of Maine at Augusta","correspondingAuthor":false,"prefix":"","firstName":"Luke","middleName":"","lastName":"Kramer","suffix":""},{"id":544470544,"identity":"4602ca65-88c4-4a1f-9882-da3f31a1e99e","order_by":4,"name":"Stephanie Miller","email":"","orcid":"","institution":"University of 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1","display":"","copyAsset":false,"role":"figure","size":111369,"visible":true,"origin":"","legend":"\u003cp\u003eThis image was used as the accompanying image to the descriptions of the Soybean Crop Model in the survey. The image shows predicted daily development stage and biomass accumulation for 1 m\u003csup\u003e2\u003c/sup\u003e of soybean plants in prime growing conditions in 2005, based on the Soybean Crop Model created by the Barracuda team.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7860451/v1/941367c1070348ab25589163.png"},{"id":98428200,"identity":"0d33cc3e-46d0-4ee5-973c-c25cbb7c4d0d","added_by":"auto","created_at":"2025-12-17 16:41:46","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":773562,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7860451/v1/8d13cba1-c84b-409c-a193-85fe1ea96152.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Using Narrative to Explain Uncertainty in Climate Change","fulltext":[{"header":"Background","content":"\u003cp\u003eClimate change and data scientists have a powerful need to identify and implement effective communication strategies for their technical work. This need is imperative as agricultural and conservation decision-makers need understandable evidence to inform their decisions. Previous research has indicated strong promise with the use of narrative communication to facilitate the understanding of complex science. This study furthers that research by exploring whether narrative communication is an effective tool for communicating uncertainty data, since decision-makers may hesitate to act if they misunderstand or lose confidence in projection models.\u003c/p\u003e\u003cp\u003eThe BARRACUDA Project (Biodiversity And RuRal Adaptation to Climate change Using Data Analysis) is an interdisciplinary, multi-state, and multi-university research project in Northern New England seeking to create high-quality data models and tools to support agriculture and conservation sectors regarding climate change decision-making (RII Track-2 FEC; NSF Award #2019470). Within Barracuda, Team CHASM (Communication Has A Special Meaning) is a mentored undergraduate and graduate research team at the University of Maine at Augusta (UMA) seeking to gain a better understanding of effective climate change communication strategies. This study uses data projection tools created within the Barracuda Project as examples of uncertain information and empirically tests participants\u0026rsquo; understanding, confidence, emotional response, and self-reported behavior change according to the use of either narrative or technical descriptions.\u003c/p\u003e"},{"header":"Literature Review","content":"\u003cp\u003eCommunication fuels scientific dissemination. Communication is a channel by which sentiment, knowledge, evidence, and value can be expressed, received, and reflected (Filiz 2020; Hansson et al. 2020). Information can be communicated in a variety of ways; however, for the purpose of this paper, narrative and technical communication styles are the focus. Technical communication has been found to limit effective translation to non-scientists (Bromme et al. 2018; Schwingel 2018). Bullock et al. (2019) illustrate how the use of jargon can hinder the processing of scientific information and amplify resistance to the message. Narrative science communication is associated with increased recall, ease of understanding and comprehension, and shorter reading time (Dahlstrom 2014). Narrative communication can also engage multiple cognitions by emotionally transporting audiences such that they become deeply immersed in the narrative (Appel et al. 2015). By making science culturally relevant and applicable through storytelling, narrative communication may bridge the gap between scientific data and common public understanding (Dahlstrom 2014; Downs 2014).\u003c/p\u003e\u003cp\u003eNarrative science communication’s power lies in its ability to make scientific concepts more relatable and memorable. Narratives engage listeners by creating a story that attracts attention, making the information more captivating and clearer to understand (Dahlstrom 2014; Downs 2014). This type of communication is particularly successful in reaching audiences who typically consume scientific data via mass media, which favors storytelling formats (Dahlstrom 2014). Thus, communication through a narrative lens can help to translate the relevance of science to everyday people, making the information actionable for audiences (Downs 2014). Through making information accessible and actionable, narratives can lead to intentional behavioral changes in participants (Kim et al. 2012).\u003c/p\u003e\u003cp\u003eHowever, to our knowledge there are limited examples of narratives used to communicate uncertainty-inherent projections related to climate change. Rydmark et al. (2020) examined how audiences struggle with the concept of scientific uncertainty, often being unsure if projections are somewhat variable or largely unknown given the disparity between how scientists and the general population tend to communicate levels of uncertainty. Decision-makers may overestimate uncertainty and postpone decisions (Horne, et al., 2021) or misdirect resources (Ward, et al. 2019) due to this miscommunication. Furthermore, van der Bles et al. (2019) argued that scientists must differentiate between epistemic uncertainty, where there may be an abundance of evidence even though unknowns remain (e.g., climate change is underway; predictions vary because no model can include all data), and aleatory uncertainty, based on too many unknown or unpredictable variables such that it is equivalent to random chance (e.g., the weather forecast twenty years from next Saturday). The application of narrative science communication in this context offers a potential resolution. Narrative science communication may help the general population more fully grasp the nuances of uncertainty to make decisions in their lives.\u003c/p\u003e"},{"header":"Methodology","content":"\u003cp\u003e The Institutional Review Board (IRB) at the University of Maine at Augusta (UMA) classified as exempted from further review (IR 30195, approved 10/3/23) pursuant to 45 CFR 46.104(d) (2). Participants were adult university students in Psychology and Communications courses, age 18 to 65 (\u003cem\u003en =\u003c/em\u003e 81). Among the participants who completed the demographic portion of the survey (\u003cem\u003en =\u003c/em\u003e 65), 69% were female; 84% were White or Caucasian; 6% were Native; 5% were Hispanic; 2% were Black; and 2% were multiracial. Informed consent was obtained from all participants, and survey measures were set to not permit participants under the age of 18 years who may be taking college-level courses to access the survey.\u003c/p\u003e\u003cp\u003eThe survey was developed and hosted within Qualtrics, a web-based survey, data collection, and analysis platform (Qualtrics, Provo, UT). Participants were provided images to represent four data projection tools developed by the greater Barracuda Project research team to support New England agricultural and conservation workers to make decisions with uncertain climate change data. For each resource image, participants were randomly assigned to view descriptions of the associated data projection tools that were written in either a narrative or technical format. Four data projection tools with accompanying descriptions were used in this study: Spotted-Wing Drosophila, Soybean Crop Model, Crop Switching, and Data Visualization (Barracuda 2023). The images provided to participants were from internal communications within the grant team used in the development and discussions around these four tools.\u003c/p\u003e\u003cp\u003eAs an example of the study components, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the image associated with the Soybean Crop Model data projection tool. Participants randomly assigned to the narrative read a comedic short story about a hapless farmer whose neighbor helps him understand why he would be better off growing soybeans as opposed to pineapples based on the current and projected growing season changes. The narrative begins with “My neighbor Jerry says I’m a fool for trying to grow pineapples in Northern New England, but I think he just doesn’t have the courage to go where no one has ever gone before.” The exasperated neighbor brings the hapless farmer the Soybean Crop Model and explains what data informs the model as well as how to understand the biomass projections.\u003c/p\u003e\u003cp\u003eParticipants randomly assigned to the technical description read the same information about the model inputs and outputs, though in a technical and data rich format. The technical descriptions are straightforward and instructive. The Soybean Crop Model description began, “The plot in panel A shows the number of days from planting it took the plot of soybeans to reach each of the five important development stages from emergence (sprouts emerging from the soil) to maturity (plants ready for harvest).”\u003c/p\u003e\u003cp\u003ePredictive models have inherent uncertainty. The epistemic uncertainty stems from the inability of any model to encompass the infinite variables that can affect the target outcome; decisions must always be made as to which variables to include or exclude in the simplified models. The narrative and technical descriptions include information about the specific data included in the model, and describes the prediction outputs. For example, the Soybean Crop Model predicts crop yield per square meter under ideal climate conditions, and is informed by temperature, solar radiation, and soil moisture content data (but not, for example, soil quality, the use of fertilizers, pesticides, crop rotations, or dozens of other factors). The aleatory uncertainty stems from the many other unpredictable factors that can also affect crop outcomes, such as deer infestations, wildfires, or bad seed stock. These factors are essentially random events that cannot be predicted in a crop yield model.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eAfter reviewing the uncertainty information, all participants were given the same questions to measure their understanding, regardless of whether they received the information in the narrative or technical format. Participants were then asked additional questions designed to measure their emotional response/transportation (immersion into a narrative) and to evaluate their judgements of confidence in the uncertainty information presented. Each of these question sets were developed by the team according to each specific data projection tool. The questions were presented as true/false to check respondents' understanding of the material and Likert-scaled questions were used to assess the confidence level of respondents after they completed the readings. The emotional response/transportation questions were adapted from a short form transportation scale (Appel et al. 2015) which measures self-reported emotional, imaginative, and cognitive engagement with the descriptions.\u003c/p\u003e\u003cp\u003eAnalysis of the data collected from this survey was conducted with PSPP software (GNU Project 2007). The survey experienced significant attrition, such that later portions of the survey had lower completion rates. Therefore, the team combined all scenarios to create a more robust sample size when analyzing the difference in outcomes between narrative and technical reporting.\u003c/p\u003e\u003cp\u003eThe overall aim of this study was to discover if narrative descriptions were more effective than technical descriptions at communicating uncertain scientific data. It was hypothesized that participants receiving the narrative versus technical description would 1) better understand, 2) have a stronger emotional response, and 3) a stronger likelihood of belief or behavior change, without 4) undermining their confidence in the uncertain data.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eRegarding hypothesis 1 (that participants will better understand the uncertain scientific data when communicated in narrative versus technical format), the one-way ANOVA revealed a modest but significantly better understanding of the science when participants read a narrative reporting formats (\u003cem\u003en\u003c/em\u003e = 153) versus technical reporting formats (\u003cem\u003en\u003c/em\u003e = 150) (F(1, 301) = 6.22, p = .013*). Each data projection tool scenario had three questions that checked for understanding of the reported science, leading to a correct understanding score between 0 (no correct answers) and 3 (all correct answers). Participants who read the narrative scenarios scored more 2s and 3s, showing increased understanding of the information (Table 1). \u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"623\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" valign=\"top\" style=\"width: 623px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 1\u003c/strong\u003e. Number of correct answers for all narrative versus all technical description responses\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 349px;\"\u003e\n \u003cp\u003eNumber of correct answers\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003eNarrative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cem\u003en\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e153\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eRow %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e2.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e16.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e37.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e43.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e100.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eColumn %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e27.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e41.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e51.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e56.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e50.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eTotal %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e1.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e8.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e19.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e22.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e50.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003eTechnical\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cem\u003en\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e150\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eRow %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e5.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e24.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e36.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e34.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e100.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eColumn %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e72.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e59.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e48.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e43.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e49.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eTotal %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e2.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e11.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e18.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e16.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e49.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cem\u003en\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e113\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e118\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e303\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eRow %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e3.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e20.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e37.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e38.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e100.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eColumn %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e100.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e100.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e100.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e100.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e100.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eTotal %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e3.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e20.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e37.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e38.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e100.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAdditionally, when participants were asked if the descriptions they read were easy or hard to understand, participants reported perceiving that the narrative explanations were easier to understand (Table 2). A Chi Square analysis revealed a significant difference in the perception of ease versus difficulty of the readings between participants who read narrative versus technical descriptions (\u0026chi;\u0026sup2; (df = 1, \u003cem\u003en\u003c/em\u003e = 258) = 9.84, p = .002**). \u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"449\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"top\" style=\"width: 449px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 2.\u003c/strong\u003e Participant perception of description difficulty\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 175px;\"\u003e\n \u003cp\u003eEasy v. Hard\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003eEasy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003eHard\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003eNarrative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cem\u003en\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e127\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eRow %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e76.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e23.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e100.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eColumn %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e56.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e35.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e49.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eTotal %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e37.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e11.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e49.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003eTechnical\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cem\u003en\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e131\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eRow %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e58.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e42.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e100.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eColumn %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e43.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e64.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e50.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eTotal %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e29.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e21.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e50.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cem\u003en\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e173\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e258\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eRow %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e67.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e32.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e100.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eColumn %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e100.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e100.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e100.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eTotal %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e67.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e32.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e100.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eHypothesis 2 (that participants would have a stronger emotional response to narrative versus technical writing) found no significant differences (F(1, 209) = 2.46, p = .118). These results indicate that participants who read the narrative descriptions were no more emotionally transported (i.e., invested and immersed) than those who read the technical descriptions.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHypothesis 3 (that participants would show a stronger likelihood of belief or behavior change) was measured by asking participants two yes or no questions: \u0026ldquo;Do you perceive this type of data projection tool would be useful to you or someone you know?\u0026rdquo; and \u0026ldquo;If you had access to this tool would you use it to inform your decision-making?\u0026rdquo; A Chi Square analysis on perceived usefulness of each respective data projection tool yielded significant results, \u0026chi;\u0026sup2; (df = 1, \u003cem\u003en\u003c/em\u003e = 277) = 15.06, p = .000** (Table 3), such that participants receiving the narrative descriptions found the tools to be more useful than did the participants reading the technical descriptions.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"449\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"top\" style=\"width: 449px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 3.\u003c/strong\u003e Perceived usefulness of the data uncertainty tools\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 175px;\"\u003e\n \u003cp\u003eUseful\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003eNarrative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cem\u003en\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e108\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e144\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eRow %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e75.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e25.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e100.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eColumn %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e60.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e36.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e52.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eTotal %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e39.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e13.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e52.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003eTechnical\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cem\u003en\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e133\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eRow %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e52.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e47.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e100.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eColumn %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e39.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e63.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e48.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eTotal %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e25.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e22.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e48.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cem\u003en\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e178\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e277\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eRow %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e64.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e35.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e100.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eColumn %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e100.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e100.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e100.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eTotal %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e64.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e35.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e100.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eHowever, believing that the tools would be useful did not lead to participants reporting a likeliness to actually utilize the tools. A Chi Square analysis did not reveal evidence of perceived likelihood of behavior change to use the tools \u0026chi;\u0026sup2; (df = 1, \u003cem\u003en\u003c/em\u003e = 282) = 1.82, p = .178 (Table 4).\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"449\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"top\" style=\"width: 449px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 4.\u003c/strong\u003e Perceived likelihood of behavior change to use the tools\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 175px;\"\u003e\n \u003cp\u003eBehavior Change\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003eNarrative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cem\u003en\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e146\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eRow %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e62.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e37.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e100.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eColumn %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e55.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e47.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e51.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eTotal %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e32.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e19.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e51.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003eTechnical\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cem\u003en\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e136\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eRow %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e54.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e45.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e100.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eColumn %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e44.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e53.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e48.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eTotal %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e26.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e22.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e48.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cem\u003en\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e165\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e117\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e282\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eRow %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e58.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e41.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e100.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eColumn %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e100.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e100.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e100.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eTotal %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e58.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e41.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e100.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eHypothesis 4 (that the use of narrative descriptions would not undermine participants\u0026rsquo; confidence in uncertain data) was this study\u0026rsquo;s new application of the use of narrative in scientific communication. The survey included confidence questions for both epistemic and aleatory uncertainty related to each data projection tool. As an example, the check in confidence held for the Soybean Crop Model despite epistemic uncertainty asked, \u0026ldquo;How confident are you that the model can predict harvestable biomass of soybeans under ideal conditions?\u0026rdquo; We hypothesized that the narrative descriptions would not undermine participants\u0026rsquo; confidence in the scientific data. Indeed, a Chi Square analysis of all combined scenarios revealed no evidence of a difference in confidence level when participants read the narrative versus technical descriptions, \u0026chi;\u0026sup2; (df = 4, \u003cem\u003en\u003c/em\u003e = 258) = 6.16, p = .187 (Table 5).\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"624\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"8\" valign=\"top\" style=\"width: 624px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 5\u003c/strong\u003e. Participant confidence in the tools when asked questions regarding epistemic uncertainty\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003eVery Confident\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eConfident\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eNeutral\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eUnconfident\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003eVery Unconfident\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003eNarrative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u003cem\u003en\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e127\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003eRow %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e11.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e53.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e29.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e5.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e100.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003eCol. %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e71.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e47.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e45.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e58.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e49.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003eTotal %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e5.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e26.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e14.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e2.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e49.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003eTechnical\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u003cem\u003en\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e131\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003eRow %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e4.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e56.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e34.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e3.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e100.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003eCol. %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e28.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e52.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e54.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e41.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e100.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e50.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003eTotal %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e2.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e28.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e17.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e1.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e50.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u003cem\u003en\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e142\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e258\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003eRow %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e8.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e55.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e31.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e4.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e100.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003eCol.%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e100.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e100.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e100.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e100.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e100.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e100.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003eTotal %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e8.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e55.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e31.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e4.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e100.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eWhile the data indicated that narrative descriptions of the science did not undermine the confidence of participants in uncertain data of an epistemic nature, the same was unfortunately true for uncertain data of an aleatory nature. For example, the check for confidence (or more specifically, overconfidence) in the Soybean Crop Model\u0026rsquo;s ability to predict unpredictable outcomes asked, \u0026ldquo;How confident are you that the model can predict harvestable biomass of soybeans under unforeseen circumstances like deer attacking the crop?\u0026rdquo; All of the models\u0026rsquo; narrative and technical descriptions included the same explicit information about what data was used in the model to create the projections. In this case of the Soybean Crop Model, both descriptions stated that the model was based on temperature and solar radiation each day, as well as the soil moisture content. Deer infestations were therefore outside of the scope of the model.\u003c/p\u003e\n\u003cp\u003eParticipants who read the technical description were more likely to recognize that the models could not account for such random factors. Participants who read the narrative descriptions were less likely to recognize that random variables were beyond the scope of the science, \u0026chi;\u0026sup2; (df = 4, \u003cem\u003en\u003c/em\u003e = 231) = 10.70, p = .03* (See Table 6). This finding is of concern because it indicates that while audience confidence in uncertainty data may not be undermined by the use of narrative scientific description, the audience\u0026rsquo;s complex understanding of the scope and implications of the uncertainties may be adversely affected.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"624\" class=\"fr-table-selection-hover\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"8\" valign=\"top\" style=\"width: 624px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 6\u003c/strong\u003e. Participant confidence in the tools when asked questions regarding aleatory uncertainty\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003eVery Confident\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eConfident\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eNeutral\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eUnconfident\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003eVery Unconfident\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003eNarrative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u003cem\u003en\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e122\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003eRow %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e34.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e52.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e9.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e2.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e100.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003eCol. %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e12.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e54.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e59.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e37.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e42.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e52.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003eTotal %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e18.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e27.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e5.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e1.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e52.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003eTechnical\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u003cem\u003en\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e109\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003eRow %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e6.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e32.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e39.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e18.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e3.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e100.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003eCol. %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e87.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e45.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e40.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e62.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e57.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e47.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003eTotal %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e3.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e15.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e18.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e8.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e1.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e47.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u003cem\u003en\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e107\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e231\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003eRow %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e3.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e33.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e46.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e13.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e3.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e100.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003eCol.%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e100%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e100.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e100.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e100.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e100.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e100.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003eTotal %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e3.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e33.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e46.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e13.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e3.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e100.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe study results indicate that narrative descriptions of scientific uncertainty data can facilitate better comprehension of information, making them a valuable tool for instructional purposes. Likewise, findings indicate that participants perceive narratives as easier to understand as opposed to technical communication. The increased comprehension validates previous research (Dahlstrom 2014; Downs 2014) that narratives can be used to promote engagement with a wider audience, increasing the accessibility of science to the general population.\u003c/p\u003e\u003cp\u003e The study found no significant difference in participants\u0026rsquo; emotional transportation. That is to say, participants who read the narrative descriptions were no more and no less transported than those who read the technical descriptions. While narrative does have an established capacity for transporting audiences to the extent that they are wholly absorbed in the story (Appel et al. 2015), the narratives used in this study did not establish that effect among participants. Future research can explore the impact of narratives that resonate more and less with participants\u0026rsquo; personal experiences, values, or concerns. Audiences who experience greater transportation may form emotional connections with the story presented, which may lead to increased behavior change or other communication outcomes in addition to increased understanding of the material.\u003c/p\u003e\u003cp\u003eThe study did not replicate the increases in behavior change associated with the use of narrative descriptions that has been found in the literature (Downs 2014; Kim et al. 2012). While participants who read the narrative descriptions found the data projection tools to be more useful than those who read the technical descriptions, they did not indicate that they would change their behavior to make use of the tool. The lack of reported behavior change may be due to the lack of transportation into the narrative, or it may be due to our participant pool being composed of college students that includes both individuals who are likely to use the data projection tool in their careers and those who are unlikely to have the need. Alternatively, the empirical survey format we used may have limited the way behavior change could be measured, and simply may not be able to capture the increase in behavior change found in the literature (Kim et al. 2012). Future research could particularly focus on populations who are more likely to have use for the tool in addition to exploring the use of more and less transporting narratives and adopting measures that more comprehensively capture behavior change.\u003c/p\u003e\u003cp\u003eThis study explored how the use of narrative in science communication might impact an audience\u0026rsquo;s understanding of and confidence in uncertain projections. Scientific uncertainty has been shown to be a specific area of struggle for audiences, with decision-makers over-emphasizing uncertain projections and subsequently delaying urgent action (Horne, et al. 2021) or misdirecting resource allocation (Ward, et al. 2019). Epistemic uncertainty in particular needs to be better understood so the public can have confidence in robust projections. This is because understanding epistemic uncertainty may help people see why a projection looks the way it does and how it can become more trustworthy over time. The fact that scientists must make informed decisions about what to include and exclude in a predictive model will necessarily leave unknowns in any model. However, each predictive model adds to a body of evidence that need not be discounted whole cloth due to ongoing epistemic uncertainty. The results indicate that audience confidence in epistemic uncertainty data was not impacted by the use of narrative descriptions. This is promising for advances in science communication.\u003c/p\u003e\u003cp\u003eThe results also included a concerning caveat. Participants who read narrative descriptions were less likely to recognize the limitations that the data projection models possess in predictions concerning random factors beyond the model scope. While there was little difference between participants reporting they were confident or very confident in the models\u0026rsquo; ability to predict truly random factors (Narrative 35.2% versus Technical 38.5%), participants who read the technical descriptions were more likely to be unconfident and very unconfident (22.0%) than those who read narrative descriptions (12.3%). It is possible that this disparity resulted from narrative descriptions invoking a sense of fictionality in participants\u0026rsquo; understanding, such that the possibilities and limitations of science were perceived to change. All narrative descriptions in this study were fictional but not fantastical, providing plausible interactions of real people with the uncertain projection tools. Nevertheless, the very use of narrative may lead audiences to suspend disbelief and entertain applications inconsistent with reality (e.g., a model predicting yield in ideal growing conditions is able to predict a deer infestation). As such, future research should explore whether suspension of disbelief can impact an audience\u0026rsquo;s cognitive adherence to scientific laws in the context of fictional or non-fictional scientific narratives. It is also possible that this disparity occurred because reading technical descriptions invoked a higher level of critical thinking, such that audiences were more likely to consider scientific implications of data when they were actively engaged with scientific language. Future research should explore whether different thought processes, including critical thinking, are engaged sufficiently with narrative science communication to sufficiently and ethically communicate uncertain information. Increased understanding of uncertainty data is crucial, but not if audiences lose the impulse to read critically and consider the realistic limits of the science.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study sought to use narrative communication as a possible solution to the challenges faced by science communicators who disseminate uncertainty projection models. It was hypothesized that participants receiving the narrative versus technical description format would 1) better understand, 2) have a stronger emotional response, and 3) a stronger likelihood of belief or behavior change, without 4) undermining their confidence in the uncertain data. We found that narrative descriptions improved participants\u0026rsquo; understanding of scientific information. In the same vein, participants also perceived narratives to be easier to understand. However, our data does not provide clear support that narrative communication can be successfully applied to the communication of uncertainty data. Participants\u0026rsquo; confidence in uncertain data did not lessen with narrative descriptions, though participants did show an overconfidence in predictions beyond the scope of the models. Similarly, our data does not support the idea that participants will alter their behavior nor will they have a strong emotional response when presented with narratives about data projection tools. In light of these findings, using narrative communication styles should be approached with an awareness of its potential pitfalls in explaining uncertainty. However, the power of narrative science communication should not be discarded. Using caution, and with additional research as to its appropriate use with uncertain data, narrative science communication can serve as an instrument to foster scientific literacy in broader communities.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003e\u003cstrong\u003eEthics approval\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eThe questionnaire and methodology for this study was approved by the Institutional Review Board committee of the University of Maine at Augusta (IR 30195) which recommend this proposal be exempted from further review pursuant to 45 CFR 46.104(d) (2) \u0026ldquo;[r]esearch only including interactions involving educational tests, survey procedures, interview procedures, or observation of public behavior\u0026rdquo;.\u003c/p\u003e\n\u003ch2\u003eCompeting Interest\u003c/h2\u003e\n\u003cp\u003eThe authors have no relevant financial or non-financial interests to disclose.\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003eConsent to participate\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eInformed consent was obtained from all individual participants included in the study.\u003c/p\u003e\n\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cp\u003eThis work was supported by a National Science Foundation Grant (RII Track-2 FEC; NSF Award #2019470).\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003eAll authors contributed to the study conception and design, including material preparation, data collection, and analysis. Stephanie Miller developed technical descriptions used in the survey. Luke Kramer, Michelle Brunton, Grace Freeman, Alina Rousseau, and Laura K. Corlew developed narrative descriptions used in the survey. Laura K. Corlew and Stephanie Miller provided academic support and professional advising at all stages. Laura K. Corlew, Grace Freeman, and Alina Rousseau wrote the main manuscript text. All authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003ch2\u003eAcknowledgement\u003c/h2\u003e\n\u003cp\u003eThe members of the Barracuda research team developed the data projection models and associated images that were used in the survey: Brian McGill, PI, Nicholas Gotelli, co-PI, Matthew Dube, Co-PI, Timothy Waring, Co-PI, Meredith Niles, Co-PI, Laurent Hebert-Dufresne, SKP\u003c/p\u003e\n\u003ch2\u003eData Availability\u003c/h2\u003e\n\u003cp\u003e[https://www.openicpsr.org/openicpsr/workspace?goToPath=/openicpsr/228762\u0026amp;amp;goToLevel=project](https:/www.openicpsr.org/openicpsr/workspace?goToPath=/openicpsr/228762\u0026amp;goToLevel=project)\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAppel, M., Gnambs, T., Green, M., \u0026amp; Richter, T. (2015). The Transportation Scale\u0026ndash;Short Form (TS\u0026ndash;SF). 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Proceedings of the National Academy of Sciences - PNAS, 111(Supplement 4), 13627\u0026ndash;13633. https://doi.org/10.1073/pnas.1317502111 \u003c/li\u003e\n\u003cli\u003eFiliz, B. (2020). The Relationship between Effective Communication Skills and Verbal Intelligence Levels of Faculty of Sport Sciences Students. International Journal of Educational Methodology, 6(3), 603-612. https://doi.org/10.12973/ijem.6.3.603 \u003c/li\u003e\n\u003cli\u003eGNU Project (2007). GNU PSPP (Version 3) [Computer Software]. Free Software Foundation. Boston, MA. Available from: https://www.gnu.org/software/pspp/\u0026quot;\u003c/li\u003e\n\u003cli\u003eHansson, S., Orru, K., Siibak, A., B\u0026auml;ck, A., Kr\u0026uuml;ger, M., Gabel, F., \u0026amp; Morsut, C. (2020). 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Enhancing Scientific Communication Through an Undergraduate Biology and Journalism Partnership. \u003cem\u003eJournal of Microbiology \u0026amp; Biology Education\u003c/em\u003e, \u003cem\u003e19\u003c/em\u003e(1). https://doi.org/10.1128/jmbe.v19i1.1445 \u003c/li\u003e\n\u003cli\u003evan der Bles, A. M., van der Linden, S., Freeman, A. L., Mitchell, J., Galvao, A. B., Zaval, L., \u0026amp; Spiegelhalter, D. J. (2019). Communicating uncertainty about facts, numbers and science. \u003cem\u003eRoyal Society Open Science\u003c/em\u003e, \u003cem\u003e6\u003c/em\u003e(5), 181870. https://doi.org/10.1098/rsos.181870 \u003c/li\u003e\n\u003cli\u003eWard, A. I., Richardson, S., Macarthur, R., \u0026amp; Mill, A. C. (2019). Using and communicating uncertainty for the effective control of invasive non‐native species. \u003cem\u003eMammal Review\u003c/em\u003e, \u003cem\u003e50\u003c/em\u003e(2), 211\u0026ndash;220. https://doi.org/10.1111/mam.12188 \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"communication, climate, description, narrative, uncertainty, epistemic, aleatory","lastPublishedDoi":"10.21203/rs.3.rs-7860451/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7860451/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study presents the findings on the use of narrative when communicating complex scientific uncertainties. A growing body of evidence in narrative cognition and communication has shown promise for the use of narrative in science communication. The project researches the use of narrative in communicating the scientific uncertainty of data projection tools built to support agricultural and conservation decision makers with decisions related to climate change. Prior research has shown uncertainty is difficult to communicate and raises ethical concerns since scientists do not want audiences to be either underconfident in projections with epistemic uncertainty (unknowns remain) or overconfident in projections with aleatory uncertainty (based on random factors). Participants completed an empirical survey reviewing four data projection tools. For each tool, participants were randomly assigned to read either a technical or narrative description of the tool. Results indicate that narrative descriptions increased understanding, though measurements for emotional response and behavior change were non-significant. Results additionally indicate that the use of narrative did not reduce participants\u0026rsquo; confidence in projections of epistemic uncertainty. However, participants did show a statistically significant overconfidence in the models\u0026rsquo; ability to predict completely random factors, indicating that caution should be applied and further research conducted on the use of narrative in communicating scientific uncertainty.\u003c/p\u003e","manuscriptTitle":"Using Narrative to Explain Uncertainty in Climate Change","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-20 16:02:51","doi":"10.21203/rs.3.rs-7860451/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ea86e2d8-e7fb-4f1e-ba06-7978781cdfa3","owner":[],"postedDate":"November 20th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":57929876,"name":"Earth and environmental sciences/Environmental social sciences"},{"id":57929877,"name":"Biological sciences/Psychology"},{"id":57929878,"name":"Social science/Psychology"}],"tags":[],"updatedAt":"2025-12-12T08:54:39+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-20 16:02:51","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7860451","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7860451","identity":"rs-7860451","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}