A researcher seeks to pass a bond issue and asks a sample of respondents who have a bachelor’s degree if they are voting in favor of the bond because it would be beneficial to the county.
This scenario represents **selection bias**, which occurs when a sample is not representative of the population being studied. In data-driven decision making, valid conclusions depend on collecting data from a sample that accurately reflects the broader population.
By surveying only respondents with a bachelor’s degree, the researcher systematically excludes other segments of the population who may have different opinions about the bond issue. Educational attainment may influence voting behavior, making the sample biased toward a particular viewpoint. As a result, the findings cannot be generalized to the entire voting population.
While the wording of the question may be persuasive, the primary statistical error is the **non-random and restricted selection of respondents**. Response bias relates to how participants answer questions, whereas this issue arises before responses are even collected. Faulty operationalization and confusion of causality are not applicable here.
Data-driven decision making stresses ethical sampling practices to avoid misleading conclusions. Therefore, the correct answer is **D**, selection bias.
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