Typically, a bulk sample is downsized to a laboratory sample through sampling, which introduces errors.

Common sampling errors include contamination and mismatches between the sampling method and the type of measurement.

Specifically,  potential errors such as concentration bias due to improper splitting and contamination from particle size reduction methods can occur. Minimizing these errors is crucial for accurate and reliable results.

Assuming the normal distribution of a target population, in the equation for the confidence interval of the sampling error, n_s represents the sample count, and s_s denotes the sampling standard deviation. Here, reorganizing and replacing e helps determine the number of samples required for the desired sampling error.

Another critical aspect is reducing the overall variance for the analysis, which relies on two components: the method and the sampling.

While gathering enough samples of the correct size improves the variance associated with sampling, increasing the number of analyses on each sample improves the method variance.