Various bias-correction methods such as EXTRA, DIGing, and exact diffusion have been proposed recently to solve distributed deterministic optimization problems. These methods employ constant step-sizes and converge linearly to the exact solution under proper conditions. However, their performance under stochastic and adaptive settings remains unclear. It is still unknown whether bias-correction is beneficial in stochastic settings. By studying exact diffusion and examining its steady-state performance under stochastic scenarios, this paper provides affirmative results. It is shown that the correction step in exact diffusion can lead to better steady-state performance than traditional methods.