This paper develops a novel wild bootstrap procedure to construct robust bias- corrected valid confidence intervals (CIs) for fuzzy regression discontinuity designs, providing an intuitive alternative to existing analytical methods. The CIs generated by this procedure are valid under conditions similar to the standard analytical procedures used in the empirical literature. Simulations provide evidence that this new method is at least as accurate as the analytical corrections when applied to a variety of data generating processes featuring heteroskedasticity, endogeneity and clustering. Finally, we demonstrate its empirical relevance by revisiting Angrist and Lavy (1999) analysis of class size on student outcomes.