Vision-based object grasping and manipulation in robotics require accurate estimation of the object 6D pose. Therefore pose estimation has received significant attention and multiple datasets and evaluation metrics have been proposed. Most of the existing evaluation metrics rank the estimated poses solely based on the visual perspective i.e. how well two geometrical surfaces are aligned, which does not directly indicate the goodness of the pose for a robot manipulation. In robotic manipulation the optimal grasp pose depends on many factors such as target object weight and material, robot, gripper, and the task itself. In this work we address these factors by proposing a probabilistic evaluation metric that ranks an estimated object pose based on the conditional probability of completing a task given this estimated pose. The evaluation metric is validated in controlled experiments and a number of baseline and recent pose estimation methods are compared on a dataset of industrial parts for assembly tasks. The experimental results confirm that the proposed evaluation metric measures the fitness of an estimated pose more accurately for a robotic task compared to prior metrics.