Large language models (LLMs) can give out erroneous answers to factually rooted questions either as a result of undesired training outcomes or simply because the world has moved on after a certain knowledge cutoff date. Under such scenarios, knowledge editing often comes to the rescue by delivering efficient patches for such erroneous answers without significantly altering the rests, where many editing methods have seen reasonable success when the editing targets are simple and direct (e.g., "what club does Lionel Messi currently play for?").However, knowledge fragments like this are often deeply intertwined in the real world, making effectively propagating the editing effect to non-directly related questions a practical challenge (e.g., "who is the offspring of the owner of the club that Messi currently plays for?"). Prior arts have coined this task as multi-hop knowledge editing with the most popular dataset being MQuAKE, serving as the sole evaluation benchmark for many later proposed editing methods due to the expensive nature of making knowledge editing datasets at scale.In this work, we reveal that up to 33% or 76% of MQuAKE's questions and ground truth labels are, in fact, corrupted in various fashions due to some unintentional clerical or procedural oversights. Our work provides a detailed audit of MQuAKE's error pattern and a comprehensive fix without sacrificing its dataset capacity. Additionally, we benchmarked almost all proposed \mquake{}-evaluated editing methods on our post-fix dataset, \mquaker{}. It is our observation that many methods try to overfit the original \mquake{} by exploiting some data-specific properties of \mquake{}. We provide a guideline on how to faithfully approach such datasets and show that a simple, minimally invasive approach can bring excellent editing performance without such exploitation. Please refer to the supplemental material for assets.