For results from a study sample to be truly generalizable to a broader population, the study must use

Question

BlackTom AI · Accepted Answer

To evaluate generalizability, we consider how the sample represents the broader population. The core idea is that a representative sample reduces bias and reflects the characteristics of the population from which it is drawn.

Option discussion (note: only one option is provided in the data):
- 'random sampling' as the method: Random sampling gives each member of the population an equal chance of selection, which tends to produce a sample that mirrors the population on average. This alignment supports external validity by limiting systematic differences between the sample and the population, thereby making results more generalizable. In practice, random sampling is the standard approach when researchers want to extend findings beyond the sample.

Why other potential approaches (if they were listed) might be problematic for generalizability:
- Nonrandom/convenience sampling: If participants are selected based on ease of access, they may differ in important ways from the broader population, introducing sampling bias and limiting generalizability.
- Quota or purposive sampling: While these can ensure certain characteristics are represented, they do not guarantee that the overall distribution matches the population, which can reduce generalizability.
- Systematic sampling without randomness: Selecting every kth individual can introduce bias if there is a hidden pattern in the population list that correlates with the outcome.

In summary, when the goal is to generalize study results to a broader population, random sampling is the approach that best supports representativeness and external validity. Since the provided options include only 'random sampling', this is the mechanism aligned with generalizability across populations.

For results from a study sample to be truly generalizable to a broader population, the study must use 单项选择题

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