In which scenario would data scientists likely need to perform additional data collection?

When key variables are missing from analysis, additional data collection becomes essential for data scientists. Missing variables can lead to incomplete insights and inaccurate conclusions, which can undermine the entire analysis. Key variables often play a critical role in forming hypotheses, understanding relationships, and making predictions. Without these variables, the model may lack the necessary depth to represent real-world phenomena accurately. By gathering more data to include these missing variables, data scientists can create a more robust model that provides clearer, more actionable insights.

Other scenarios such as providing sufficient results or handling very large data sets do not inherently necessitate further data collection. Additionally, budget concerns, while critical in a project, don't directly relate to the need for additional data to ensure the analysis is comprehensive and effective. Hence, the necessity for filling gaps in key variables distinctly highlights the need for further data collection.