Benchmark practices in the mining industry include the establishment of Quality Programs — Quality Assurance and Quality Control (QAQC) — to monitor the correct execution of sampling protocols and control each stage of the ‘Sample Cycle’: Sample Collection, Preparation (comminution) and Analysis. QAQC reports commonly include statistical-numerical results that quantify performance of QAQC controls (field duplicates, preparation duplicates, blanks, standards etc). Graphics such as scatter plots, QQ plots, histograms and cumulative frequencies are used to represent the results. Statistical values including relative difference, absolute difference, relative variance, averages, T-test, and Z_x0002_scores are used to quantitatively express the relationship between duplicate pairs ... but is an effective quality program just a statistical exercise? This paper aims to highlight the concept of ‘Quality Management’ (QM) as the precursor of continuous monitoring (ideally in real time) of QAQC results on Exploration, Production, Processing and Port samples, determined by trend analysis performed on a time and/or grades basis. Currently, there is a paradigm in the process of quality assurance that if individual data points fall within a predetermined acceptance limit then they are ‘acceptable’ and therefore suitable for informing operational and investment decisions. However, what is stated with respect to QM is that sometimes results found within the acceptance limits can indeed be internally biased, or show material deviations over a period of time, thereby impacting reconciliation results and/or potentially creating discrepancies with the final buyers. QM refers to the proactive detection of these ‘anomalous tendencies’, the approach and communication with the source of the information, the understanding of the reasons that cause the deficiencies, and the generation of agreed action plans to remediate the situation and ensure the consistency of results, in other words: continuous improvement on proactive manners.