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A correct evaluation of scheduling algorithms and a good understanding of their optimization criterias are key components of resource management in HPC. In this work, we discuss bias and limitations of the most frequent optimization metrics from the literature. We provide elements on how to evaluate performance when studying HPC batch scheduling. We experimentally demonstrate these limitations by focusing on two use-cases: a study on the impact of runtime estimates on scheduling performance, and the reproduction of a recent highimpact work that designed an HPC batch scheduler based on a network trained with reinforcement learning. We demonstrate that focusing on quantitative optimization criterion ("our work improve the literature by X%") may hide extremely important caveat, to the point that the results obtained are opposed to the actual goals of the authors. Key findings show that mean bounded slowdown and mean response time are irrelevant objectives in the context of HPC. Despite some limitations, mean utilization appears to be a good objective. We propose to complement it with its standard deviation in some pathologic cases. Finally, we argue for a larger use of area-weighted response time, that we find to be a very relevant objective.