In which scenario is modeling used for inference?

Modeling used for inference involves drawing conclusions or insights about a larger population based on a sample of data. In the context of the choices provided, identifying the largest risk factor in a clinical trial clearly exemplifies this concept. Researchers analyze the data collected from trial participants to infer which factors are significantly associated with a specific outcome, such as the effectiveness of a treatment. This process of inference allows them to generalize findings beyond the sample and recognize patterns or relationships that may apply to a broader population.

In contrast, while house valuation involves modeling, it primarily relies on prediction rather than inference about a broader population. The advertising company’s focus on targeting marketing demographics is more about segmentation and application of data rather than making inferences about underlying factors affecting behavior. Estimating player salaries can involve predictive modeling as well, focusing on forecasting future outcomes or values rather than inferring wider relationships within a population. Therefore, the scenario related to clinical trials best fits the definition of inference in statistical modeling.