Here are the essential concepts you must grasp in order to answer the question correctly.
Stratified Sampling
Stratified sampling is a technique where the population is divided into distinct subgroups, or strata, that share similar characteristics. In the given study, students are categorized by their class year (freshman, sophomore, junior, senior), and a random sample is taken from each class. This method ensures that each subgroup is represented in the sample, which can lead to more accurate and generalizable results.
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Random Sampling
Random sampling is a method where each member of a population has an equal chance of being selected for the sample. In the study, after dividing students into classes, a random sample is chosen from each class. This approach minimizes selection bias and helps ensure that the sample reflects the diversity of the entire population, enhancing the validity of the findings.
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Sampling Bias
Sampling bias occurs when certain members of a population are systematically more likely to be selected for the sample than others, leading to an unrepresentative sample. In the context of the study, if only one class year was overrepresented or underrepresented, it could skew the results. Understanding this concept is crucial for evaluating the reliability of the study's conclusions about attitudes toward smoking.
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