This study shows that, while most of participants reported similar results for different performance metrics calculated from GCD and CV datasets, some groups reported incorrect values due to the use of incorrect formulae during analysis. As a result, we remind the community of the correct analysis formulae to ensure more reliable reporting of performance metrics going forwards. In addition, different valid interpretations of "specific capacitance" between laboratories resulted in a range of values being reported for this performance metric. To avoid confusion going forwards, researchers should clarify their interpretation of "specific capacitance" when reporting values. Furthermore, the impact of different practices in data analysis becomes more significant for electrochemical profiles showing less ideal capacitive behaviour. We support previous recommendations that non-ideal datasets should not be analysed using formulae for ideal supercapacitors, in which charge is solely stored via non-Faradaic adsorption of ions in the electric double-layer, to avoid inaccurate reporting of performances, and capacity should be reported for such devices. In the future, establishing an accessible and standardised open-access analysis protocol for calculating performance metrics of fast-charging energy devices is required to improve the consistency of analysis and reporting. An agreed computational analysis program could benefit the community by further eliminating variations caused by subjectivity in manual analysis. Further efforts are recommended to consider the key findings of this study when developing, for example, an optimized machine-learning algorithm that automatically derives the relevant key data from various data files and different testing conditions. Such an "approved" tool, especially when being part of open science, would enormously reduce the variation seen from today's use of individual approaches toward supercapacitor data analysis.