Sampling, by definition, is a statistical analysis tool used to analyze a subset of a larger data set. You can think of it as a representative snapshot of a larger population.
Sampling comes with many advantages. Besides not having to conduct a study with the entire population, sampling allows us to gather insights in a cost-effective, time efficient manner. Additionally, different sample sizes come with different advantages. Smaller samples allow for quicker analysis turnarounds while larger sample sizes provide increased accuracy. Statistically, the larger sample size, the smaller margin of error, and vice versa.
The type of sampling utilized often depends on the data set and the goals and objectives of a research study. The most common types of sampling options include:
- Random Sampling: Software is utilized to randomly select subjects from the whole population.
- Stratified Sampling: Smaller sets of data are created from the original data set based on a common factor (i.e. hair color), and samples are randomly collected from each group.
- Cluster Sampling: The larger data set is divided up into subsets (clusters), then a random sampling of clusters is analyzed.
- Systematic Sampling: A sample is created in which an interval is used to extract data from the larger population. For example, if using an Excel spreadsheet, setting an interval of every ten rows to extract data would be systematic sampling.