Which sampling method does not yield data generalizable to the larger population?

• A. Convenience Sampling
• B. Simple Random Sampling
• C. Stratified Sampling
• D. Cluster Sampling

Answer: (A)

Generalizability depends on how well the sample represents the whole population. Convenience sampling selects participants who are easiest to reach, not a random or systematic representation of the group. This introduces selection bias, because the people available may share specific traits that differ from the population at large. As a result, findings from a convenience sample are unlikely to apply broadly beyond that group.

In contrast, simple random sampling gives everyone an equal chance to be included, which tends to produce a sample that mirrors the population. Stratified sampling ensures key subgroups are represented, improving generalizability across those subgroups. Cluster sampling, when clusters are chosen randomly and samples are drawn within those clusters, can approximate population characteristics if the clusters are representative and numerous. So, the method that most clearly limits generalizability is the convenience approach.