It is crucial to have consistent reporting of performance data between different researchers in this field. Many reports have emerged over the past decade discussing best practices for data analysis and reporting for energy storage devices . These studies have primarily focused on reporting the correct formulae and methods for data analysis. However, there is yet to be an empirical study investigating whether researchers in the field are correctly implementing these recommendations and assessing the variation in data analysis and reporting between different laboratories. Here, we address this issue by conducting the first interlaboratory study to assess the variation in reporting of performance metrics from galvanostatic charge-discharge (GCD) and cyclic voltammetry (CV) datasets for lab-scale fast-charging supercapacitor devices, including devices that display both ideal and non-ideal capacitive behaviour. This study does not address variation in experimental supercapacitor assembly, but only the data analysis of pre-supplied data sets. From this study, we conclude that, while most groups obtained similar results despite differences in analysis methods, misuse of formulae could lead to incorrect values being reported. Furthermore, different interpretations of terminology between laboratories can result in different values being reported for a given performance metric, potentially confusing researchers who are new to the field. In addition, the variation in results is amplified if the device shows non-ideal capacitive behaviour, highlighting that calculating capacitance for such systems is more challenging than for devices displaying more ideal capacitive behaviour. From these insights, we reinforce correct analysis procedures and make several further recommendations to ensure consistent analysis and reporting of performance metrics across researchers. This should significantly benefit the field in the future.