Which statement best defines the difference between probability and prediction in the context of FAIR analysis?

In the context of FAIR analysis, probability is fundamentally about estimating the likelihood of various potential outcomes. It quantifies how likely certain events or outcomes are to occur based on available data and statistical methods. This means that probability does not pinpoint a single result but rather provides a range of possible outcomes, reflecting uncertainty and variability. This concept is pivotal in risk assessment, where understanding the range of potential risks helps in making informed decisions.

On the other hand, predictions are typically more definitive statements regarding what will happen in the future, often based on patterns identified in historical data. While predictions may utilize statistical models that include probabilistic elements, they do not encapsulate the uncertainty in the same way that probability does.

By recognizing this distinction, it becomes clear why expressing probability as a range of probable outcomes aligns well with how FAIR analysis operates. It emphasizes understanding risk through the lens of uncertainty, rather than assuming a certain outcome, which is crucial for effective risk management.