Which sampling method is characterized by identifying cases strictly on chance?

The sampling method characterized by identifying cases strictly on chance is simple random sampling. In this method, every individual in the population has an equal and independent chance of being selected. This process ensures that the sample chosen is representative of the larger population without any biases. The randomness associated with this technique is crucial as it minimizes selection bias, allowing for more accurate and generalizable findings in research.

Simple random sampling can be achieved through various means, such as using random number generators or drawing names from a hat, reinforcing the notion that each selection is purely by chance. This aspect makes it a fundamental method in statistical sampling and research design, ideal for establishing the basis for probability sampling techniques.

In contrast, methods like stratified sampling and quota sampling involve more structured approaches where specific criteria are used to ensure different segments of the population are represented in the sample. Cluster sampling also involves selection based on predefined groups rather than purely random choice, further distinguishing it from simple random sampling.