Final answer:
A population is the entire group of interest to researchers while a sample is a subset actually studied. Random sampling is essential for a generalizable sample by giving everyone an equal chance of selection. Sampling bias occurs when the sampling method leads to non-generalizable results.
Step-by-step explanation:
A population is the entire group of people that a researcher is interested in, whereas a sample is the smaller subset of that group that actually participates in the research. To ensure a generalizable set of participants, it is best to use random sampling. Sampling that leads to a non-generalizable set of participants is referred to as sampling bias.
In the context of surveys and studies, the population consists of the overall group of individuals that the researchers want to learn about, which could be a very large and diverse group depending on the study's aim. A sample, in turn, is a smaller but adequate subset from this population that the researchers actually include in their study. The goal is to have the sample mirrored as closely as possible to the larger population to ensure that the findings can apply broadly.
Random sampling is crucial for obtaining a representative sample, as this method gives each member of the population an equal chance of being selected. This contrasts with methods that might introduce sampling bias, where some members of the population might have a higher or lower chance of being included in the sample, possibly skewing the results and undermining the study's validity. To reduce the chance of bias and ensure the sample reflects the overall population, researchers may also use stratified sampling, dividing the population into groups and ensuring each group is proportionately represented.