Conditional probability is a critical component of decision trees, as it helps quantify the likelihood of various outcomes based on specific conditions or events. In a decision tree, each branch represents a possible decision or outcome, and the associated probabilities reflect the chances of those outcomes occurring given particular circumstances. By integrating conditional probabilities, decision-makers can assess how the occurrence of one event influences the probabilities of subsequent events, allowing for a more accurate evaluation of potential scenarios. This integration is essential for assessing the likelihood of different outcomes because it enables decision-makers to understand not only the immediate effects of their choices but also how these choices might lead to subsequent events, thereby providing a comprehensive view of the potential risks and rewards associated with each alternative. Ultimately, using conditional probability within decision trees enhances the quality of decision-making by facilitating a deeper analysis of the interplay between choices and outcomes under uncertainty.