From a conceptual standpoint, a sample assembled in a completely random fashion will be more REPRESENTATIVE of the actual population. Always remember that inferential statistics are conducted on samples to make INFERENCES BACK TO THE POPULATION. With a randomized sample, all of the biodiversity that exists in the real world has a better chance of being accounted for in the statistical analyses.
Random selection (every member of a given population has an equal chance of being selected for the study) and random assignment (selected participants are randomly allocated to either the treatment or control group) are the primary components of probability sampling.
There are three types of probability sampling:
1. Simple random sampling - Every member of a population has an equal chance of being selected for participation in the study.
2. Stratified random sampling - Independent strata within a given population are randomly sampled. Each stratum must be overtly defined and homogeneous in some relative way. Simple random sampling is then conducted on the stratum (singular) or strata (plural) of interest.
3. Clustered random sampling - Naturally occurring or defined subgroups of a given population are randomly sampled. The subgroups need to be defined and are often grouped according to socioeconomic, demographic, clinical, or theoretical characteristics.
Non-probability sampling is used in observational research designs. The lack of randomization in these designs introduces selection and observation biases that can greatly skew the inferences yielded from statistics.
There are two types of non-probability sampling techniques:
1. Convenience sampling is the most prevalent form of non-probability sampling. Researchers just access retrospective data available to them in their empirical or clinical environment, or via existing databases, and conduct statistical analyses.
2. Purposive sampling is a more focused approach to sampling where specific groups of individuals are targeted for participation in the study.