Crowdsourced query processing is an emerging processing technique that tackles computationally challenging problems by human intelligence. The basic idea is to decompose a computationally challenging problem into a set of human friendly microtasks (e.g., pairwise comparisons) that are distributed to and answered by the crowd. The solution of the problem is then computed (e.g., by aggregation) based on the crowdsourced answers to the microtasks. In this work, we attempt to revisit the crowdsourced processing of the top-k queries, aiming at (1) securing the quality of crowdsourced comparisons by a certain confidence level and (2) minimizing the total monetary cost. To secure the quality of each paired comparison, we employ two statistical tools, Student's t-distribution estimation and Stein's estimation, to estimate the confidence interval of the underlying mean value, which is then used to draw a conclusion to the comparison. Based on the pairwise comparison process, we attempt to minimize the monetary cost of the top-k processing within a Select-Partition-Rank framework. Our experiments, conducted on four real datasets, demonstrate that our stochastic method outperforms other existing top-k processing techniques by a visible difference.