Final answer:
To apply research results directly to a population, a random sample is crucial because it allows each person an equal chance to be included, thereby accurately representing the population.
Step-by-step explanation:
If you want to apply your research results directly to a population, it is especially crucial to use a random sample. This sampling method is essential because it gives each individual in the population an equal chance of being included, thereby providing the most accurate representation of the entire population. This is opposed to other methods such as convenience, quota, or stratified samples, which might introduce bias or be representative only of specific segments of the population.
A random sample ensures that every element has the same opportunity to be selected, minimizing bias and allowing for generalizations about the population from the sample. In contrast, a convenience sample is easy to collect but might not accurately represent the population, and a quota or stratified sample could have inherent biases based on the selected strata.
Other forms of random sampling include stratified sampling, where the population is divided into strata, and then samples are taken from each stratum, and cluster sampling, where clusters are formed based on characteristics like location or demographic qualities. However, for direct applicability of research results to a population, a simple random sample is the most robust method.