Which best describes a comprehensive strategy to mitigate missing data effects?

• A. Rely solely on one data source
• B. Ignore missing data
• C. Assume perfect information
• D. Acknowledge gaps, consider bounding estimates, use alternate data, scenario planning, test sensitivity to missing data, and document limitations

Answer: (D)

Mitigating missing data effects is about building a robust, transparent approach that treats data gaps as part of the analysis, not something to pretend away. The best strategy acknowledges what is missing, bounds what the missing information could plausibly imply, and brings in alternative data sources to triangulate findings. It also uses scenario planning to imagine how different patterns of gaps might affect conclusions and applies sensitivity analyses to see how results shift under different missing-data assumptions. Documenting the limitations of the data and the methods used ensures others can reproduce and trust the findings. This combination reduces bias, improves robustness, and communicates uncertainty clearly. In contrast, relying on a single data source, ignoring missing data, or assuming information is perfect all fail to account for uncertainty and can lead to overconfident or biased decisions.