Automated planning systems are increasingly deployed in real-world applications, often characterised by uncertainty and noise stemming from sensors, actuators, and environmental conditions. Under such circumstances, improving the deployability of generated plans requires assessing their robustness to varying conditions, thereby reducing the need for costly replanning. Replanning can be computationally intensive and may hinder the practical applicability of planning systems. In many domains, such as urban traffic control or underwater exploration, it is often sufficient for plans to reach an acceptable region rather than the exact goal. A key distinction in this context lies between valid plans (which achieve the intended goal under ideal conditions) and executable plans (which remain feasible under uncertainty or perturbation). This paper formalises the notion of execution-invariant planning tasks, in which plans are robust to noise and uncertainty. To foster the adoption of automated planning in real-world settings, we propose a statistical framework for evaluating plan robustness, offering a quantifiable measure of a plan’s ability to reach a goal within a specified tolerance under diverse perturbations or uncertainty. We validate our approach in two real-world domains, demonstrating its effectiveness.