In recent years, iterative reconstruction methods have been investigated extensively with the aim of reducing radiation dose while maintaining image quality in CT exams. In such a case, redundant data is usually available. In conventional FBP-type reconstructions, redundant data has to be carefully treated by applying a redundant weighting factor, such as Parker weighting. However, such a redundant weight has not been fully studied in a statistical iterative reconstruction framework. In this work, both numerical simulations and in vivo data sets were analyzed to study the impact of redundant weighting schemes on the reconstructed images for both static and moving objects. Results demonstrated that, for a static object, there was no obvious difference in the iterative reconstructions using different redundant weighting schemes, because the redundant data was consistent, and therefore, they all converged to the same solution. On the contrary, for a moving object, due to the inconsistency of the data, different redundant weighting schemes converged to different solutions, depending on the weight given to the data. The redundant weighting, if appropriately selected, can reduce motion-induced artifacts.