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In product quality assurance, many concepts are used based on the assumption of normality of manufacturing processes, but often the analysis of this data is carried out either by skipping some mandatory steps, or by misunderstanding some concepts, or by confusing some. The paper aims to highlight that the complex statistical analysis tools used by various dedicated programs allow the simultaneous calculation of several indicators through which the quality of processes is assessed, so the software provides the computing power for these indicators, but the human operator must manage the statistical control report, because the software does not intervene to "refuse" the calculation of some indicators when certain conditions are not previously met. For this purpose, a case study is presented with a data set processed through two types of control sheets for individual data (Individuals – Moving Range and Individuals – Moving Average), revealing that the final control limits involve going through several stages through which data generated by special causes are eliminated and only after this can the normality of the data be verified and, if there is normality, the capability indicators can be calculated. It is found that, going through the successive stages of establishing control limits, as outliers are eliminated, the control limits become closer and closer. So, the control sheet verifies the stability of the process, and only if the process is stable can we proceed to verify the normality of process and, finally, determining the process capability. In this way, a review of the errors that can be encountered in statistical analyses of process stability was carried out and explanations were provided for all of them.