In antimicrobial testing, as in all testing, the integrity and accuracy of test results requires the proper selection of testing methods and adherence to the protocols written in said testing method. When a standard method is available to measure a particular characteristic or technology, it should be used. This approach ensures that results are reliable, reproducible, and fit-for-purpose, as standard methods are meticulously developed to serve specific testing needs.

Standard methods, by their very design, are created to meet particular objectives. The title of a method indicates its intended purpose. For instance, ASTM E3162 was explicitly developed to measure the durability of antimicrobial technologies to laundering. This method provides a robust and precise evaluation of how antimicrobial finishes withstand the rigors of repeated washing, making it the go-to standard for such assessments.

However, in practice, some individuals incorrectly opt to use alternative test methods like AATCC TM135 to evaluate the durability of antimicrobial technologies. AATCC TM135 was originally designed to measure dimensional changes in textiles during home laundering. It is not suitable for assessing the durability of antimicrobial technologies due to several critical reasons. The procedure includes steps that may introduce bacterial or chemical contamination, or even cross-contamination between samples, thus compromising the validity of the results.

Similarly, AATCC TM61, designed to test for colorfastness, is often misapplied in antimicrobial testing. While a limited subset of Test 2A within AATCC TM61 aligns closely with ASTM E3162 and indeed served as a foundation for its development, the full method does not comprehensively address the specific needs of antimicrobial durability testing.

Choosing the correct standard method is paramount in antimicrobial testing to ensure that the results are both meaningful and accurate. Misapplying methods designed for other purposes not only undermines the integrity of the results but also may lead to incorrect conclusions and potentially harmful decisions based on faulty data.