What type of sampling involves selecting participants randomly from a defined population?

Random sampling is a method where participants are selected entirely by chance from a defined population, which ensures that every member of the population has an equal probability of being chosen. This technique is crucial for maintaining the representativeness of the sample, minimizing selection bias, and allowing for valid generalizations about the larger population. Because participants are selected randomly, this approach helps researchers draw conclusions that will be more applicable to the broader group being studied.

Other sampling methods, such as convenience sampling, involve selecting individuals who are readily available, which can introduce bias and limit the generalizability of the findings. Stratified sampling involves dividing the population into subgroups and then randomly sampling from those subgroups, which, while useful for ensuring representation of specific characteristics, is not purely random selection of individuals from the entire population. Systematic sampling employs a fixed, periodic selection method, such as choosing every nth person from a list, rather than a purely random process. Thus, random sampling is distinguished by its emphasis on equal probability of selection across the population.