Sampling in research is the process of selecting individual units of a group or population for analysis to represent a larger group. By selecting and analyzing a subset through sampling, a researcher is able to generalize results from a smaller (and easier to analyze) population to a larger group. This process saves time, effort, and money. Depending on the type of research, sampling can come in many different forms.

**Simple random sampling** of a population seeks to develop a subset of a population which fairly and accurately represents the larger group. In random sampling, the goal is that each unit will have an equal chance of being selected. To produce this sample, a researcher will rely on the random selection of units through a random number generator, random number formula, or electronic device. From a population of 1000, a simple random sample will choose units from the entire population as a whole.

**Hierarchical random sampling **of a population is similar to simple random sampling, but divides the original population into sections instead of sampling from the whole. From a population of 1000, a simple random sample will choose units from subsections of the population. For instance, a researcher may choose to randomly sample from the first 500 units and then randomly sample from the next 500 units. This type of random sampling is also referred to as **stratified random sampling. **

**Systematic random sampling **is a type of sampling in which a researcher chooses units based on a specific interval. From a population of 1000, a researcher may choose to sample every 20th unit.

Finally, **clustered sampling **is used in spatial analyses to sample from a geographic area rather than a population. In clustered sampling, a researcher will divide an area into smaller sections and sample from the individual sections. The methods for creating bounding boxes for the subsections varies depending on the area, but is often dependent on either population concentration or geographic feature.