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A researcher might choose to apply marginalization in their analysis primarily to simplify the process of dealing with complex data structures. This technique allows them to focus on specific variables of interest while integrating out others, which can lead to a clearer understanding of the relationships within the data.

By marginalizing over certain dimensions, researchers can derive results that emphasize the most relevant aspects of their model without the noise or influence of less pertinent variables. This can be particularly beneficial in high-dimensional datasets where the complexity can obscure important insights.

The other options, while potentially relevant in different contexts, do not capture the main purpose of marginalization as clearly. Enhancing parameter estimates is not the primary goal, as marginalization is more about integration and focus rather than improving estimates directly. Avoiding the need to compute the entire dataset could be a secondary effect but is not the main motivation behind applying marginalization, which is more about clarity and relevance. Likewise, while eliminating outliers can be an important aspect of data analysis, marginalization is not specifically a method for this purpose. Therefore, the choice that best reflects the intent behind marginalization is centered on focusing on the part of the data that is most relevant.