Sampling is a crucial step in analytical chemistry, allowing researchers to collect representative data from a large population. Common sampling methods include random, judgmental, systematic, stratified, and cluster sampling.

Random sampling is a method where each member of the population has an equal chance of being selected for the sample. It involves selecting individuals randomly, often using random number generators or lottery-type methods. For example, when analyzing the properties of a polymer sheet, random sampling can be employed by dividing the sheet into equal 1 cm x 1 cm sections and then using a random number table to select 10 sections at random for analysis. This ensures the selection of unbiased samples across the sheet. Random sampling minimizes bias and helps produce a sample that accurately represents the entire population.

Judgmental sampling, also known as purposive or selective sampling, is a non-probability method that relies on the researcher's judgment or expertise to choose individuals or samples deemed most suitable for the study. For example, suppose a researcher is studying the bioaccumulation of polychlorinated biphenyls (PCBs) in fish. In that case, they may selectively sample only fish that meet specific criteria, such as being smaller or appearing unhealthy. This method is often used when regulatory agencies require specific criteria to define the sample being collected.

Systematic sampling involves selecting every n^th individual from a population after randomly choosing a starting point. For example, in environmental studies, systematic sampling can be used to collect samples from a lake by dividing it into a grid and taking samples at regular intervals from each section, ensuring spatial coverage across the lake. This approach helps in studies involving spatial trends, such as oxygen distribution in water.

Stratified sampling involves dividing the population into distinct subgroups or strata based on specific characteristics relevant to the research. Samples are then randomly selected from each stratum in proportion to their representation in the population. For instance, when studying particulate matter in urban air, stratified sampling could be employed by dividing the population into particle size categories (e.g., fine, coarse). Samples are then taken from each particle size group to assess their contribution to overall pollution levels. This method provides more precise analysis within each subgroup and reduces sampling error.

Cluster sampling involves dividing the population into clusters or groups, ideally mini-representations of the population. A few clusters are randomly chosen, and everyone within the selected clusters is sampled. This method is commonly used in large-scale studies where logistical constraints limit the number of sampling sites. For example, a study on groundwater contamination might divide a region into clusters based on geographic boundaries and then randomly select a few clusters (regions) for sampling all available wells.