Crowd-sourcing is increasingly being used for largescale polling and surveys. Companies such as SurveyMonkey and Instant.ly make crowd-sourced surveys commonplace by making the crowd accessible through an easy-to-use UI with easy to retrieve results. Further, they do so with a relatively low latency by having dedicated crowds at their disposal. In this paper we argue that the ease with which polls can be created conceals an inherent difficulty: the survey maker does not know how many workers to hire for their survey. Asking too few may lead to samples sizes that do not look impressive enough. Asking too many clearly involves spending extra money, which can quickly become costly. Existing crowd-sourcing platforms do not provide help with this, neither, one can argue, do they have any incentive to do so. In this paper, we present a systematic approach to determining how many samples (i.e. workers) are required to achieve a certain level of statistical significance by showing how to automatically perform power analysis on questions of interest. Using a range of queries we demonstrate that power analysis can save significant amounts of money and time by often concluding that only a handful of results are required to arrive at a decision. We have implemented our approach within InterPoll, a programmable developer-driven polling system that uses a generic crowd (Mechanical Turk) as a back-end. InterPoll automatically performs power analysis by analyzing both the structure of the query and the data that it dynamically polls from the crowd. In all of our studies we obtain statistically significant results for under $30, with most costing less than $10. 1. Our approach saves both time and money for the sur- 2. vey maker. 3.