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Stratified sampling is a method of sampling that involves dividing a population into distinct subgroups, or strata, that share similar characteristics. The goal is to ensure that each subgroup is adequately represented in the sample. This technique is particularly useful when researchers want to analyze specific segments of a population, such as age groups or income levels, to draw more accurate conclusions.

Systematic sampling is a method where researchers select members of a population at regular intervals after an initial random start. For example, if a researcher decides to sample every 10th individual from a list, they would randomly select a starting point and then continue selecting every 10th person. This method is different from stratified sampling, as it does not focus on specific subgroups but rather on a uniform selection process.

In research, true/false statements are often used to assess understanding of concepts or methods. Determining the truth value of a statement requires a clear understanding of the definitions and applications of the terms involved. In this case, recognizing the distinction between stratified and systematic sampling is essential to accurately evaluate the statement and provide a correct revision if necessary.