Classifying species into risk categories is a ubiquitous process in conservation decision-making affecting regulatory procedures, conservation actions, and guiding resource allocation at global, national, and regional scales. However, monitoring programs often do not provide data required for accurate species classification decisions. Misclassification can lead to otherwise preventable species extinctions, undue regulatory burden, poor allocation of limited conservation resources, and can undermine species conservation legislation. We developed a framework that evaluates monitoring designs based on the ability to correctly inform a species classification decision, where minimizing the risk of misclassification is the central objective. We further evaluated monitoring designs by calculating the expected value of information and explored the relationship between statistical power to detect trends and misclassification. Our measure of misclassification risk, which can be tailored to the decision context, clarified the costs of over- and under-protection. High power to detect trends often corresponded to accurate species classification decisions. However, in several scenarios power to detect trends was low but the ability to correctly inform the classification decision was high. The value of information generally increased with monitoring intensity and quantified the tradeoffs between spatial and temporal replication. Our framework allows managers to assess monitoring program performance with direct implications for conservation decision-making. Our framework affords practitioners an opportunity to evaluate the effectiveness of monitoring programs a priori focusing on improving conservation decisions. We demonstrate that prioritizing monitoring to minimize misclassification errors can improve monitoring efficiency and conservation decision-making with considerable practical applications and benefits for species conservation.