How Scientists Are Learning to Communicate What They Don't Know
Imagine a weather forecaster who confidently predicts a 20% chance of rain. One person hears this and grabs an umbrella; another leaves it at home. Same information, different decisions.
How we interpret probability in daily decisions reveals our relationship with uncertainty.
Life-or-death decisions depend on accurately communicating what we know and what we don't.
Now magnify this scenario to life-or-death situations: predicting the path of a hurricane, estimating pandemic infection rates, or assessing the safety of a new food additive. In each case, how scientists communicate what they don't know becomes as crucial as what they do know.
Uncertainty isn't a flaw in science—it's an inherent part of how we understand complex systems. Yet for decades, a profound communication gap has persisted between scientists who quantify uncertainty and the decision-makers who must act on that information.
From climate models to economic forecasts, miscommunicated uncertainty can lead to everything from personal inconvenience to policy failures with global consequences. The good news? Researchers are now developing innovative strategies to bridge this gap, creating a new science of communicating the unknown.
A revealing 2025 study exposed just how wide the communication gap between scientists and decision-makers has become. Through in-depth interviews with 17 scientists and 15 decision-makers, researchers discovered fundamentally different approaches to uncertainty 1 .
Scientists, trained in methodological rigor, typically express uncertainty through technical vocabulary and probabilistic language. They might say something is "statistically significant at the 95% confidence level" or present results with "confidence bounds" 1 .
Decision-makers—whether government officials, emergency managers, or corporate leaders—prioritize actionable insights. They need uncertainty communicated in ways that directly support decision-making across diverse contexts 1 .
| Aspect | Scientists' Approach | Decision-Makers' Needs |
|---|---|---|
| Primary Focus | Methodological rigor | Actionable insights |
| Communication Style | Technical, probabilistic | Practical, contextual |
| View of Uncertainty | Inherent in models | Factor in decision process |
| Key Concern | Being precisely correct | Making timely decisions |
The challenges run deeper than mere terminology. Research reveals that people consistently struggle to interpret probabilistic information, especially when percentages or statistical terms are involved 1 . This isn't a intelligence issue—it's a cognitive one. Our brains aren't naturally wired to think in probabilities.
The European Food Safety Authority (EFSA) recognizes this challenge, noting that even qualitative terms like "negligible," "low," or "high" uncertainty are interpreted differently by different people 2 . Quantifying uncertainty on a percentage scale reduces ambiguity, but introduces other comprehension challenges 2 .
Before exploring solutions, it's crucial to understand what we mean by uncertainty. Economists like Professor Bo Sun draw a critical distinction between risk and uncertainty 3 .
Describes situations where we know the possible outcomes and can assign probabilities to each. Think of rolling a die—you know all possible outcomes and their exact probabilities 3 .
Involves both unknown outcomes and unknown probability distributions. Events like Brexit, the COVID-19 pandemic, or major technological disruptions fall into this category—we didn't even know the full range of possible outcomes, let alone their probabilities 3 .
| Uncertainty Type | Definition | Examples | Appropriate Communication |
|---|---|---|---|
| Statistical Uncertainty | Quantifiable variation in data | Measurement imprecision | Confidence intervals, standard deviation |
| Model Uncertainty | Limitations of scientific models | Climate projections, economic forecasts | Scenario analysis, model ensembles |
| Deep Uncertainty | Unknown outcomes and probabilities | Pandemic emergence, technological disruption | Narrative scenarios, precautionary principle |
The 2025 study on scientist-decision-maker dynamics provides valuable insights into what works—and what doesn't—in uncertainty communication. The researchers employed reflective thematic analysis of 32 interview datasets, using an iterative coding process with multiple cycles of refinement to identify key themes 1 .
Researchers identified scientists with expertise in developing or using hazard, risk, or climate models, primarily from government-funded research institutes, universities, and private research consultancies 1 .
Decision-makers involved in emergency management within central and regional government, local authorities, and related agencies were selected 1 .
Semi-structured qualitative interviews were conducted, allowing flexibility and depth in exploring perspectives. Scientists were interviewed between May and August 2022, while decision-makers were interviewed between June and August 2021 1 .
Interview transcripts underwent coding using NVivo software, followed by identification of sub-themes through reflective thematic analysis 1 .
The results revealed that the core issue wasn't simply technical language, but fundamental differences in how each group perceives the purpose of uncertainty information. Scientists viewed uncertainty communication as providing complete technical context, while decision-makers needed curated information tailored to specific decisions 1 .
One promising approach involves developing user-focused uncertainty visualizations. Rather than presenting raw probabilities, effective visualizations contextualize uncertainty in decision-relevant terms.
Showing a hurricane path as a single line with "confidence bounds."
Single path with error margins
Displaying multiple possible paths with intensity gradients indicating probability.
Multiple scenarios with probabilities
The European Food Safety Authority has developed a practical framework for quantifying uncertainty in scientific assessments, using a probability scale adapted from the Intergovernmental Panel on Climate Change 2 :
Successfully communicating uncertainty requires both technical understanding and strategic approaches. Based on research findings, here are essential tools for effective uncertainty communication:
Professor Bo Sun emphasizes "anchor your decision with a structured, coherent framework that quantifies uncertainty and select strategies tailored to the specific context" 3 . This systematic approach reduces the psychological toll that uncertainty imposes on decision-makers.
As shown in the EFSA framework, standardized probability scales bridge technical and non-technical audiences by providing both quantitative ranges and qualitative descriptions 2 .
Moving beyond traditional error bars to decision-relevant visual representations that show how uncertainty affects specific choices or outcomes 1 .
EFSA's development of uncertainty guidance combined expertise from social scientists, natural scientists, and communicators, recognizing that effective communication requires diverse perspectives 2 .
Simple phrases like "based on current evidence" or "our confidence in this prediction is..." that normalize uncertainty rather than hiding it.
Digital platforms that allow decision-makers to explore different scenarios and understand how uncertainty affects outcomes in their specific context.
As we navigate increasingly complex global challenges—from climate change to pandemics to economic turbulence—effectively communicating scientific uncertainty has never been more critical.
The goal isn't to eliminate uncertainty, but to communicate it so effectively that decision-makers can act with appropriate confidence despite the unknowns. This requires scientists to move beyond technical precision toward practical relevance, and decision-makers to develop greater comfort with probabilistic thinking.
Perhaps the most important insight comes from reframing how we view uncertainty itself. As Professor Sun notes, the goal is to "develop systematic approaches that allow us to harness uncertainty, not just survive it" 3 . In a world of increasing complexity, the honest acknowledgment of uncertainty isn't a weakness—it's a mark of sophistication and the foundation of both good science and wise decisions.
The next time you hear a weather forecast, consider the sophisticated communication challenges behind that simple percentage. That 20% chance of rain represents not just atmospheric science, but the evolving art of communicating what we know, what we don't, and why both matter for the decisions we make today.