Reliable sampling practices are the key to optimising the market value of a mining or chemical company's stock. As a result, reliable sampling standards are needed. However, current standards on sampling present the universal' Pierre Gy's Sampling Theory in an ambiguous way. For example, the Total Sampling Error is divided into its basic components only in informative annexes, when it should clearly be the main body of the standard. Sampling standards are presently loaded with statistical concepts that actually belong in the annexes. Furthermore, many non-probabilistic sampling procedures are still mentioned which do not belong in a standard. The objective is not to criticise existing sampling standards, but to suggest better ones. Sections on the Sampling Theory should divide the Total Sampling Error into a sum of its nine basic components. Then, and only then, should they be addressed one at the time. Sections for establishing a sampling protocol should address the in situ Nugget Effect, the Fundamental Error, the Grouping and Segregation Error, the Long Range Quality Fluctuation Error, and the Periodic Quality Fluctuation Error. Sections for sampling methods, mechanical sampling, manual sampling, grab sampling, sampling of trucks, railroad cars, bags, and drums should be part of the critically important practical implementation of a recommended sampling protocol, called Sampling Correctness. It includes the increment Delimitation Error, the increment Extraction Error, the Weighting Error, and the Preparation Errors. Unless these errors are addressed one at a time, in an unambiguous way, the standard will remain ineffective in solving sampling problems for the industry. Therefore, a strategy is needed to create better standards required at the early stages of new projects, both by manufacturers of sampling equipment and by engineering firms. A logical sequence of important points that cannot be ignored in any sampling standard is presented. An analysis of the practical use of the words sampling uncertainty versus sampling error is recalled. Finally, as a practical example, an in-depth analysis of the correctness of cross-belt samplers is presented, and emphasis is placed on the correct ways to perform bias-tests for mechanical sampling systems.