Homogeneity testing for a candidate reference material typically involves replicated measurements on multiple units or subsamples of the material.
Homogeneity tests for CRMs follow planned experimental designs. Because the experiment is intended to test for (or estimate the size of) variation in value between different CRM units, the designs are chosen to allow separation of variation in results due to random measurement error and variation due to differences between units of the CRM. Among the simplest designs recommended for this purpose is a simple balanced nested design (see schematic).  Typically 10-30 CRM units are taken from the batch at random; stratified random sampling is recommended so that the selected units are spread across the batch. An equal number of subsamples  (usually two or three) is then taken from each CRM unit and measured. Subsamples are measured in random order. Other designs, such as randomized block designs, have also been used for CRM certification.
Data processing for homogeneity tests usually involves a statistical significance test for  evidence of differences between units of the candidate CRM. For the simple balanced design above, this typically uses an F test following ANOVA. A check for trends with production order is also recommended.
This approach is not taken in ISO Guide 35:2017; rather, emphasis is placed on deciding whether the between-unit standard deviation is sufficiently small for the intended end use. If statistical tests are used, however, the homogeneity experiment should be capable of detecting important heterogeneity, ISO Guide 35:2017 in turn requiring a sufficient combination of precision of the measurement procedure, number of RM units and number of replicates per unit. Statistical power calculations can assist in ensuring a sufficiently effective test .
In extreme cases, such as microanalysis, materials must be checked for homogeneity on sub-micron scales; this may involve much larger numbers of observations and adjustments to statistical analysis.