Which example best illustrates epistemic uncertainty in risk messaging?

• A. Natural fluctuations in weather.
• B. Limited data on a new pathogen.
• C. Unknown outcomes due to measurement error.
• D. Abundant, high-quality data reducing uncertainty.

Answer: (B)

Epistemic uncertainty comes from gaps in what we know—lack of data, incomplete information, or imperfect models that could be improved with more evidence. In risk messaging, this means communicating where our understanding is uncertain because we simply don’t have enough information yet.

Limited data on a new pathogen best illustrates this. When a pathogen is new, we don’t yet know its transmission dynamics, severity, or real-world impacts with confidence. The uncertainty exists because knowledge is incomplete, and it would shrink as more data and studies become available.

In contrast, natural weather fluctuations reflect inherent variability that exists even with perfect knowledge (aleatory uncertainty). Unknown outcomes due to measurement error point to data quality issues—uncertainty arising from how we measure rather than what we know about the phenomenon. And abundant, high-quality data reducing uncertainty shows the opposite effect: it decreases epistemic uncertainty rather than illustrating it.