**Core Concept**
Stratified sampling is a probability sampling method that involves dividing the population into subgroups or strata, based on relevant characteristics, and then sampling from each subgroup to ensure that the sample is representative of the population.

**Why the Correct Answer is Right**
Stratified sampling is ideal when the population has distinct subgroups, such as age, gender, or socioeconomic status. By sampling from each subgroup, researchers can ensure that the sample is representative of the population and that the results are not biased towards a particular subgroup. This is particularly useful in studies where the outcome variable is influenced by the subgroup variable, such as in epidemiological studies where the outcome variable is disease prevalence and the subgroup variable is age.

**Why Each Wrong Option is Incorrect**
**Option A:** Simple random sampling is a type of probability sampling where every individual in the population has an equal chance of being selected. While simple random sampling can be used in any type of population, it may not be as effective in populations with distinct subgroups, as it does not account for the subgroup structure.
**Option B:** Convenience sampling involves selecting participants based on ease of access, which can lead to biased samples. Stratified sampling, on the other hand, ensures that the sample is representative of the population by sampling from each subgroup.
**Option C:** Cluster sampling involves dividing the population into clusters and sampling from each cluster. While cluster sampling can be used in populations with distinct subgroups, it may not be as effective as stratified sampling in ensuring that the sample is representative of the population.

**Clinical Pearl / High-Yield Fact**
Remember that stratified sampling is ideal when the population has distinct subgroups, and that it ensures that the sample is representative of the population by sampling from each subgroup.

**Correct Answer: D.**