Growing behavioral evidence suggests that animals and humans represent uncertainty about both high and low-level sensory stimuli in the brain for probabilistic inference and learning. One proposal about the nature of the neural basis of this representation of uncertainty suggests that instantaneous membrane potentials of cortical sensory neurons correspond to statistical samples from a probability distribution over possible features those neurons represent. In this framework, the quality of the representation critically depends on the number of samples drawn, and hence on the time available to perform a task. This implies a strong link between the available time and the reliability of the representation. We tested this hypothesis in an orientation matching experiment with two distinct types of stimuli: circles consisting of 1-4 Voronoi patches, each filled in with a number of gray-scale Gabor wavelets with their orientations sampled from a Gaussian distribution with a different mean orientation; and 2-10 differently oriented line segments spaced evenly on a circle. After 2 seconds of stimulus presentation subjects were asked to match the orientation of one of the patches or lines in the stimulus, and indicate their certainty about the correctness of the orientation match. To test our predictions, we manipulated the number of samples on a trial-to-trial basis by varying the time available to respond. Without time constraints, subjects’ performance and certainty judgment were significantly correlated independent of the number of patches or lines the stimuli consisted of. With a decrease in available time, subjects’ orientation and certainty judgments followed the theoretically predicted trends. Importantly, a decrease in response time lead to a decrease in correlation between performance and uncertainty, even though the performance remained unchanged. Therefore, limiting the response time, and consequently the number of samples drawn, significantly influences the quality of uncertainty representation in accord with the sampling hypothesis.